Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

Prediction and explanation for ozone variability using cross-stacked ensemble learning model

With the development of monitoring technology, the variety of ozone precursors that can be detected by monitoring stations has been increased dramatically. And this has brought a great increment of information to ozone prediction and explanation studies. This study completes feature mining and reconstruction of multi-source data (meteorological data, conventional pollutant data, and precursors data) by using a machine learning approach, and built a cross-stacked ensemble learning model (CSEM). In the feature engineering process, this study reconstructed two VOCs variables most associated with ozone and found it works best to use the top seven variables with the highest contribution. The CSEM includes three base models: random forest, extreme gradient boosting tree, and LSTM, learning the parameters of the model under the integrated training of cross-stacking. The cross-stacked integrated training method enables the second-layer learner of the ensemble model to make full use of the learning results of the base models as training data, thereby improving the prediction performance of the model. The model predicted the hourly ozone concentration with R2 of 0.94, 0.97, and 0.96 for mild, moderate, and severe pollution cases, respectively; mean absolute error (MAE) of 4.48 μg/m3, 5.01 μg/m3, and 8.71 μg/m3, respectively. The model predicted ozone concentrations under different NOx and VOCs reduction scenarios, and the results show that with a 20 % reduction in VOCs and no change in NOx in the study area, 75.28 % of cases achieved reduction and 15.73 % of cases got below 200 μg/m3. In addition, a comprehensive evaluation index of the prediction model is proposed in this paper, which can be extended to any prediction model performance comparison and analysis. For practical application, machine learning feature selection and cross-stacked ensemble models can be jointly applied in ozone real-time prediction and emission reduction strategy analysis.

Quantifying anthropogenic emission of iron in marine aerosol in the Northwest Pacific with shipborne online measurements

Anthropogenic emissions are recognized as significant contributors to atmospheric soluble iron (Fe) in recent years, which may affect marine primary productivity, especially in Fe-limited areas. However, the contribution of different emission sources to Fe in marine aerosol has been primarily estimated by modeling approaches. Quantifying anthropogenic Fe based on field measurements remains a great challenge. In this study, online multi-element measurements and Positive Matrix Factorization (PMF) were combined for the first time to quantify sources of atmospheric Fe and soluble Fe in the Northwest Pacific during a cruise in spring 2015. Fe concentration in 624 atmospheric PM2.5 samples measured online was 74.58 ± 90.87 ng/m3. The PMF results showed anthropogenic activities, including industrial coal combustion, biomass burning, and maritime transport, were important in this region, contributing 31.4 % of atmospheric Fe on average. In addition, anthropogenic Fe concentration resolved by PMF was comparable to the simulation results of the CMAQ (Community Multiscale Air Quality) and GEOS-Chem (Goddard Earth Observing System-Chemical transport) models, with better correlation to CMAQ (r = 0.76) than GEOS-Chem (r = 0.26). This study developed a new method to estimate atmospheric soluble Fe, which integrates Fe source apportionment results and Fe solubility from different sources. Soluble Fe concentration was estimated as 3.93 ± 5.14 ng/m3, of which 87.0 % was attributed to anthropogenic emissions. Notably, ship emission alone contributed 27.5 % of soluble Fe, though its contribution to total Fe was only 2.2 %. Finally, the total deposition fluxes of atmospheric Fe (37.11 ± 38.43 μg/m2/day) and soluble Fe (1.85 ± 2.13 μg/m2/day) were estimated. This study developed a new methodology for quantifying contribution of anthropogenic emissions to Fe in marine aerosol, which could greatly help the assessment of impacts of human activities on marine environment.

Building resilience to extreme weather events in Phoenix: Considering contaminated sites and disadvantaged communities

The interplay of contaminated sites, climate change, and disadvantaged communities are a growing concern worldwide. Worsening extreme events may result in accidental contaminant releases from sites and waste facilities that may impact nearby communities. If such communities are already suffering from environmental, economic, health, or social burdens, they may face disproportionate impacts. Equitable resilience planning to address effects of extreme events requires information on where the impacts may be, when they may occur, and who might be impacted. Because resources are often scarce for these communities, conducting detailed modeling may be cost-prohibitive. By considering indicators for four sources of vulnerability (changing extreme heat conditions, contaminated sites, contaminant transport via wind, and population sensitivities) in one holistic framework, we provide a scientifically robust approach that can assist planners with prioritizing resources and actions. These indicators can serve as screening measures to identify communities that may be impacted most and isolate the reasons for these impacts. Through a transdisciplinary case study conducted in Maricopa County (Arizona, USA), we demonstrate how the framework and geospatial indicators can be applied to inform plans for preparedness, response, and recovery from the effects of extreme heat on contaminated sites and nearby populations. The indicators employed in this demonstration can be applied to other locations with contaminated sites to build community resilience to future climate impacts.

Spatio-temporal fusion of meteorological factors for multi-site PM2.5 prediction: A deep learning and time-variant graph approach

In the field of environmental science, traditional methods for predicting PM2.5 concentrations primarily focus on singular temporal or spatial dimensions. This approach presents certain limitations when it comes to deeply mining the joint influence of multiple monitoring sites and their inherent connections with meteorological factors. To address this issue, we introduce an innovative deep-learning-based multi-graph model using Beijing as the study case. This model consists of two key modules: firstly, the 'Meteorological Factor Spatio-Temporal Feature Extraction Module'. This module deeply integrates spatio-temporal features of hourly meteorological data by employing Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) for spatial and temporal encoding respectively. Subsequently, through an attention mechanism, it retrieves a feature tensor associated with air pollutants. Secondly, these features are amalgamated with PM2.5 concentration values, allowing the 'PM2.5 Concentration Prediction Module' to predict with enhanced accuracy the joint influence across multiple monitoring sites. Our model exhibits significant advantages over traditional methods in processing the joint impact of multiple sites and their associated meteorological factors. By providing new perspectives and tools for the in-depth understanding of urban air pollutant distribution and optimization of air quality management, this model propels us towards a more comprehensive approach in tackling air pollution issues.

O3 transport characteristics in eastern China in 2017 and 2021 based on complex networks and WRF-CMAQ-ISAM

Increasingly prominent pollution levels and strong regional characteristics of O₃, especially in economically developed eastern China, called for a regional cooperation strategy based on transport quantification. This study adopted the complex networks to construct the O₃ Transport Network (OTN) to explore characteristics in eastern China in the summer of 2017 and 2021, whose results were afterward verified with spatial source apportionment results simulated with WRF-CMAQ-ISAM. As OTN suggested, O₃ transport showed stronger and faster characteristics in eastern China in 2021 than in 2017, judging from changes in the network density, number of connections, transport ranges, and transport paths. Among all cluster communities, inland Shandong was the most important O₃ transport hub, the Central Community was the largest community, and the Southern Community showed the closest inter-city transport relationships. In- and out-weighted degrees in OTN showed relatively superior consistency with the transport matrix obtained with WRF-CMAQ-ISAM, and can be explained by wind fields. Generally, O₃ pollution in the whole eastern China showed more frequent intra-regional transport and more strengthened inter-city correlations in 2021 than in 2017, meanwhile, northerly and southerly cities exhibited strengthening and weakening trends in O₃ transport, respectively. Despite the completely different principles of complex networks and air quality models, their results were mutually verifiable. This study presented a comprehensive understanding of O₃ transport in eastern China for further formulation of regional collaborative strategies and provided the methodological verification for applying complex networks in the atmospheric environment field.

Improving the accuracy of O3 prediction from a chemical transport model with a random forest model in the Yangtze River Delta region, China

Due to inherent errors in the chemical transport models, inaccuracies in the input data, and simplified chemical mechanisms, ozone (O₃) predictions are often biased from observations. Accurate O₃ predictions can better help assess its impacts on public health and facilitate the development of effective prevention and control measures. In this study, we used a random forest (RF) model to construct a bias-correction model to correct the bias in the predictions of hourly O₃ (O₃-1h), daily maximum 8-h O₃ (O₃-Max8h), and daily maximum 1-h O₃ (O₃-Max1h) concentrations from the Community Multi-Scale Air Quality (CMAQ) model in the Yangtze River Delta region. The results show that the RF model successfully captures the nonlinear response relationship between O₃ and its influence factors, and has an outstanding performance in correcting the bias of O₃ predictions. The normalized mean biases (NMBs) of O₃-1h, O₃-Max8h, and O₃-Max1h decrease from 15.8%, 20.0%, and 17.0.% to 0.5%, -0.8%, and 0.1%, respectively; correlation coefficients increase from 0.78, 0.90, and 0.89 to 0.94, 0.95, and 0.94, respectively. For O₃-1h and O₃-Max8h, the original CMAQ model shows an obvious bias in the central and southern Zhejiang region, while the RF model decreases the NMB values from 54% to -1% and 34% to -4%, respectively. The O₃-1h bias is mainly caused by the bias of nitrogen dioxide (NO₂). Relative humidity and temperature are also important factors that lead to the bias of O₃. For high O₃ concentrations, the temperature bias and O₃ observations are the major reasons for the discrepancy between the model and the observations.

Modeling past and future spatiotemporal distributions of airborne allergenic pollen across the contiguous United States

Exposures to airborne allergenic pollen have been increasing under the influence of changing climate. A modeling system incorporating pollen emissions and atmospheric transport and fate processes has been developed and applied to simulate spatiotemporal distributions of two major aeroallergens, oak and ragweed pollens, across the contiguous United States (CONUS) for both historical (year 2004) and future (year 2047) conditions. The transport and fate of pollen presented here is simulated using our adapted version of the Community Multiscale Air Quality (CMAQ) model. Model performance was evaluated using observed pollen counts at monitor stations across the CONUS for 2004. Our analysis shows that there is encouraging consistency between observed seasonal mean concentrations and corresponding simulated seasonal mean concentrations (oak: Pearson = 0.35, ragweed: Pearson = 0.40), and that the model was able to capture the statistical patterns of observed pollen concentration distributions in 2004 for most of the pollen monitoring stations. Simulation of pollen levels for a future year (2047) considered conditions corresponding to the RCP8.5 scenario. Modeling results show substantial regional variability both in the magnitude and directionality of changes in pollen metrics. Ragweed pollen season is estimated to start earlier and last longer for all nine climate regions of the CONUS, with increasing average pollen concentrations in most regions. The timing and magnitude of oak pollen season vary across the nine climate regions, with the largest increases in pollen concentrations expected in the Northeast region.

Decarbonization will lead to more equitable air quality in California

Air quality associated public health co-benefit may emerge from climate and energy policies aimed at reducing greenhouse gas (GHG) emissions. However, the distribution of these co-benefits has not been carefully studied, despite the opportunity to tailor mitigation efforts so they achieve maximum benefits within socially and economically disadvantaged communities (DACs). Here, we quantify such health co-benefits from different long-term, low-carbon scenarios in California and their distribution in the context of social vulnerability. The magnitude and distribution of health benefits, including within impacted communities, is found to varies among scenarios which reduce economy wide GHG emissions by 80% in 2050 depending on the technology- and fuel-switching decisions in individual end-use sectors. The building electrification focused decarbonization strategy achieves ~15% greater total health benefits than the truck electrification focused strategy which uses renewable fuels to meet building demands. Conversely, the enhanced electrification of the truck sector is shown to benefit DACs more effectively. Such tradeoffs highlight the importance of considering environmental justice implications in the development of climate mitigation planning.

Air quality is found to be more equitable through two salient decarbonization pathways for California in 2050 with the relative justice of decarbonization scenarios quantified at the neighborhood level and the tradeoffs between pathways evaluated.

Using wildland fire smoke modeling data in gerontological health research (California, 2007-2018)

Widespread population exposure to wildland fire smoke underscores the urgent need for new techniques to characterize fire-derived pollution for epidemiologic studies and to build climate-resilient communities especially for aging populations. Using atmospheric chemical transport modeling, we examined air quality with and without wildland fire smoke PM_2.5. In 12-km gridded output, the 24-hour average concentration of all-source PM_2.5 in California (2007-2018) was 5.16 μg/m³ (S.D. 4.66 μg/m³). The average concentration of fire-PM_2.5 in California by year was 1.61 μg/m³ (~30% of total PM_2.5). The contribution of fire-source PM_2.5 ranged from 6.8% to 49%. We define a "smokewave" as two or more consecutive days with modeled levels above 35 μg/m³. Based on model-derived fire-PM_2.5, 99.5% of California's population lived in a county that experienced at least one smokewave from 2007 to 2018, yet understanding of the impact of smoke on the health of aging populations is limited. Approximately 2.7 million (56%) of California residents aged 65+ years lived in counties representing the top 3 quartiles of fire-PM_2.5 concentrations (2007-2018). For each year (2007-2018), grid cells containing skilled nursing facilities had significantly higher mean concentrations of all-source PM_2.5 than cells without those facilities, but they also had generally lower mean concentrations of wildland fire-specific PM_2.5. Compared to rural monitors in California, model predictions of wildland fire impacts on daily average PM_2.5 carbon (organic and elemental) performed well most years but tended to overestimate wildland fire impacts for high-fire years. The modeling system isolated wildland fire PM_2.5 from other sources at monitored and unmonitored locations, which is important for understanding exposures for aging population in health studies.

Response of aerosol composition to the clean air actions in Baoji city of Fen-Wei River Basin

The implementation of air pollution control measures could alter the compositions of submicron aerosols. Identifying the changes can evaluate the atmospheric responses of the implemented control measures and provide more scientific basis for the formulation of new measures. The Fen-Wei River Basin is the most air polluted region in China, and thereby is a key area for the reduction of emissions. Only limited studies determine the changes in the chemical compositions of submicron aerosols. In this study, Baoji was selected as a representative city in the Fen-Wei River Basin. The compositions of submicron aerosols were determined between 2014 and 2019. Organic fractions were determined through an online instrument (Quadrupole Aerosol Chemical Speciation Monitor, Q-ACSM) and source recognition was performed by the Multilinear Engine (ME-2). The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was also employed to evaluate the contributions of emissions reduction and meteorological conditions to the changes of submicron aerosol compositions. The results indicate that the mass concentrations of submicron aerosols have been substantially decreased after implementation of air pollution control measures. This was mainly attributed to the emission reductions of sulfur dioxide (SO₂) and primary organic aerosol (POA). In addition, the main components that drove the pollution episodes swapped from POA, sulfate, nitrate and less-oxidized organic (LO-OOA) in 2014 to nitrate and more-oxidized OOA (MO-OOA) in 2019. Due to the changes of chemical compositions of both precursors and secondary pollutants, the pollution control measures should be modernized to focus on the emissions of ammonia (NH₃), nitrogen oxides (NO_x) and volatile organic compounds (VOC_s) in this region.

Heterogeneous HONO formation deteriorates the wintertime particulate pollution in the Guanzhong Basin, China

Despite implementation of strict emission mitigation measures since 2013, heavy haze with high levels of secondary aerosols still frequently engulfs the Guanzhong Basin (GZB), China, during wintertime, remarkably impairing visibility and potentially causing severe health issues. Although the observed low ozone (O₃) concentrations do not facilitate the photochemical formation of secondary aerosols, the measured high nitrous acid (HONO) level provides an alternate pathway in the GZB. The impact of heterogeneous HONO sources on the wintertime particulate pollution and atmospheric oxidizing capability (AOC) is evaluated in the GZB. Simulations by the Weather Research and Forecast model coupled with Chemistry (WRF-Chem) reveal that the observed high levels of nitrate and secondary organic aerosols (SOA) are reproduced when both homogeneous and heterogeneous HONO sources are considered. The heterogeneous sources (HET-sources) contribute about 98% of the near-surface HONO concentration in the GZB, increasing the hydroxyl radical (OH) and O₃ concentration by 39.4% and 22.0%, respectively. The average contribution of the HET-sources to SOA, nitrate, ammonium, and sulfate in the GZB is 35.6%, 20.6%, 12.1%, and 6.0% during the particulate pollution episode, respectively, enhancing the mass concentration of fine particulate matters (PM_2.5) by around 12.2%. Our results suggest that decreasing HONO level or the AOC becomes an effective pathway to alleviate the wintertime particulate pollution in the GZB.

The Heavy Particulate Matter Pollution During the COVID-19 Lockdown Period in the Guanzhong Basin, China

Nationwide restrictions on human activities (lockdown) in China since 23 January 2020, to control the 2019 novel coronavirus disease pandemic (COVID-19), has provided an opportunity to evaluate the effect of emission mitigation on particulate matter (PM) pollution. The WRF-Chem simulations of persistent heavy PM pollution episodes from 20 January to 14 February 2020, in the Guanzhong Basin (GZB), northwest China, reveal that large-scale emission reduction of primary pollutants has not substantially improved the air quality during the COVID-19 lockdown period. Simultaneous reduction of primary precursors during the lockdown period only decreases the near-surface PM_2.5 mass concentration by 11.6% (12.6 μg m^-3), but increases ozone (O₃) concentration by 9.2% (5.5 μg m^-3) in the GZB. The primary organic aerosol and nitrate are the major contributor to the decreased PM_2.5 in the GZB, with the reduction of 28.0% and 21.8%, respectively, followed by EC (10.1%) and ammonium (7.2%). The increased atmospheric oxidizing capacity by the O₃ enhancement facilitates the secondary aerosol (SA) formation in the GZB, increasing secondary organic aerosol and sulphate by 6.5% and 3.3%, respectively. Furthermore, sensitivity experiments suggest that combined emission reduction of NO_X and VOCs following the ratio of 1:1 is conducive to lowering the wintertime SA and O₃ concentration and further alleviating the PM pollution in the GZB.

Source contribution analysis of PM2.5 using Response Surface Model and Particulate Source Apportionment Technology over the PRD region, China

Identifying the emission source contributions to PM_2.5 is essential for a sound PM_2.5 pollution control policy. In this study, we conduct a comparative analysis of PM_2.5 source contributions over the Pearl River Delta (PRD) region of China using two advanced source contribution modeling techniques: Response Surface Model (RSM) and Particulate Source Apportionment Technology (PSAT). Our comparative analyses show that RSM and PSAT can both reasonably predict the contribution of primary PM_2.5 emission sources to PM_2.5 formation due to its linear nature. For the secondary PM_2.5 formed by the nonlinear reactions among PM_2.5 precursors, however, our study shows that PSAT appears to have limitations in quantifying the nonlinear contribution of PM_2.5 precursors to emission reductions, while RSM seems to better address the nonlinear relationship among PM_2.5 precursors (e.g., PM_2.5 disbenefits due to local NO_x emission reductions in major cities with high NO_x emissions). The pilot study case results show that for the ambient PM_2.5 in the central cities (Guangzhou, Shenzhen, Foshan, Dongguan, and Zhongshan) of the PRD, the regional source emissions contribute the most by 42-66%; the dust emissions are the top contribution sources (29-34% by RSM and 27-31% by PSAT), and the mobile sources are listed as the secondary contributors accounting for 16-25% by RSM and 19-30% by PSAT among the anthropogenic emission sources. The city-scale cooperation on emission reductions and the enhancement of dust and mobile emission control are recommended to effectively reduce the ambient PM_2.5 concentration in the PRD.

Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species

Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, and PM2.5 constituents: SO42-, NO3-, NH4+, EC and OC) between 2005 and 2014 for the continental United States with little spatial or temporal bias. R2 ranged from 0.45 to 0.96, depending upon pollutant. Additional analysis found that temporal R2 ranged from 0.84 to 0.99 and spatial R2 values ranged from 0.76 to 0.96 across species. Four-fold cross-validation was performed to assess the model's predictive power, and ranged from 0.40 for PM10 to 0.94 for SO4 with other pollutants falling within this range. Largest improvements were found for PM10 which had substantial bias in the CMAQ fields that varied east-to-west; smallest improvements were for SO4 which was already well simulated. The random forest model results to correct the simulation biases, while largely consistent year-to-year, did show slight variation due in part to changes in the distribution of monitors and changes in CMAQ simulation inputs.

Sulfur and Nitrogen Depositions in BULGARIA—Model Results and Observations

Atmospheric deposition processes are of primary importance for human health, forests, agricultural lands, aquatic bodies, and ecosystems. South-East Europe is still characterized by numerous hot spots of elevated sulfur deposition, despite the reduction in European emission sources. The purpose of this study is to discuss the results from two chemical transport models and observations for wet and dry depositions of sulfur (S), reduced nitrogen (RDN) and oxidized nitrogen (OXN) in Bulgaria in 2016–2017. The spatial distribution and the domain main deposition values by EMEP MSC-W (model of the MSC-W Centre of the Co-operative Programme for Monitoring and Evaluation of the Long-range Transmissions of Air Pollutants in Europe) and BgCWFS (Bulgarian Chemical Weather Forecast System) demonstrated S wet depositions to be higher than N depositions, and identified a rural area in south-east Bulgaria as a possible hot-spot. The chemical analysis of deposition samples at three sites showed a prevalence of sulfate in the western part of the country, and prevalence of Cl and Na at a coastal site. The comparison between modeled and observed depositions demonstrated that both models captured the prevalence of S wet depositions at all sites. Better performance of BgCWFS with an average absolute value of the normalized mean bias of 16% was found.

An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data

Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM2.5 forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM2.5 concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM2.5 forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts.

Using Bayesian time-stratified case-crossover models to examine associations between air pollution and "asthma seasons" in a low air pollution environment

Many areas of the United States have air pollution levels typically below Environmental Protection Agency (EPA) regulatory limits. Most health effects studies of air pollution use meteorological (e.g., warm/cool) or astronomical (e.g., solstice/equinox) definitions of seasons despite evidence suggesting temporally-misaligned intra-annual periods of relative asthma burden (i.e., “asthma seasons”). We introduce asthma seasons to elucidate whether air pollutants are associated with seasonal differences in asthma emergency department (ED) visits in a low air pollution environment. Within a Bayesian time-stratified case-crossover framework, we quantify seasonal associations between highly resolved estimates of six criteria air pollutants, two weather variables, and asthma ED visits among 66,092 children ages 5–19 living in South Carolina (SC) census tracts from 2005 to 2014. Results show that coarse particulates (particulate matter <10 μm and >2.5 μm: PM_10-2.5) and nitrogen oxides (NO_x) may contribute to asthma ED visits across years, but are particularly implicated in the highest-burden fall asthma season. Fine particulate matter (<2.5 μm: PM_2.5) is only associated in the lowest-burden summer asthma season. Relatively cool and dry conditions in the summer asthma season and increased temperatures in the spring and fall asthma seasons are associated with increased ED visit odds. Few significant associations in the medium-burden winter and medium-high-burden spring asthma seasons suggest other ED visit drivers (e.g., viral infections) for each, respectively. Across rural and urban areas characterized by generally low air pollution levels, there are acute health effects associated with particulate matter, but only in the summer and fall asthma seasons and differing by PM size.

Development of a high-performance machine learning model to predict ground ozone pollution in typical cities of China

High ozone concentrations have adverse effects on human health and ecosystems. In recent years, the ambient ozone concentration in China has shown an upward trend, and high-quality prediction of ozone concentrations has become critical to support effective policymaking. In this study, a novel hybrid model combining wavelet decomposition (WD), a gated recurrent unit (GRU) neural network and a support vector regression (SVR) model was developed to predict the daily maximum 8 h ozone. We used the ground ozone observation data in six representative megacities across China from Jan. 1, 2015 to Jun. 15, 2020 for model training, and we used data from Jun. 15 to Dec. 31, 2020 for model testing. The results show that the developed model performs very well for megacities; against observations, the model obtains an average cross-validated R² (coefficient of determination) ranging from 0.90 for Shanghai to 0.97 for Chengdu in the one-step predictions, thereby indicating that the model outperformed any single algorithm or other hybrid algorithms reported. The developed model can also capture high ozone pollution episodes with an average accuracy of 92% for the next five days in inland cities. This study will be useful for the environmental health community to prevent high ozone exposure more efficiently in megacities in China and shows great potential for accurate ozone prediction using machine learning approaches.

Local and transboundary transport contributions to the wintertime particulate pollution in the Guanzhong Basin (GZB), China: A case study

Heavy haze with high levels of fine particulate matters (PM_2.5) frequently engulfs the Guanzhong Basin (GZB) in northwestern China during wintertime. Although it is an enclosed basin with a narrow opening to the east, prevailing easterly winds during heavy haze episodes have a large potential to bring air pollutants to the GZB from the two highly polluted neighboring provinces of Shanxi and Henan (SX&HN). The source-oriented WRF-Chem model simulations of a persistent and heavy haze episode that occurred in the GZB from December 6 to 21, 2016, reveal that local emissions dominate PM_2.5 concentrations in the GZB, with an average near-surface PM_2.5 contribution of about 56.0% during the episode. The transboundary transport of emissions from SX&HN accounts for around 22.2% of the total PM_2.5 in the GZB. Furthermore, with the deterioration of the air quality in the GZB from being slightly polluted to severely polluted in terms of hourly PM_2.5 concentration, transboundary transport of emissions from SX&HN plays an increasingly important role in the particulate pollution, with the average PM_2.5 contribution increasing from 8.0% to 27.5%. Compared with the source-oriented method (SOM), the brute force method (BFM) overestimates the contribution of GZB local emissions and transboundary transport of emissions from SX&HN to the total PM_2.5 in the GZB. In addition, the BFM-estimated NH₃ contribution of transboundary transport of emissions from SX&HN is negative, indicating the limitation of the BFM in source apportionment. Our results suggest that cooperative emission mitigation strategies with neighboring provinces are beneficial for lowering the particulate pollution in the GZB, particularly under severely polluted conditions.

Source impact and contribution analysis of ambient ozone using multi-modeling approaches over the Pearl River Delta region, China

Quantification of source impacts and contributions is a key element for the design of effective air pollution control policies. In this study, O₃ source impacts and contributions were comprehensively assessed over the Pearl River Delta (PRD) region of China using brute-force method (BFM), response surface modeling with BFM (RSM-BFM) and differential method (RSM-DM) respectively, high-order decoupled direct method (HDDM), and ozone source apportionment technology (OSAT). The multi-modeling comparison results indicated that under typical nonlinear atmospheric conditions during the O₃ formation, BFM, RSM-BFM, and HDDM seemed to be appropriate for assessing the impact of single source emissions; however, the results of HDDM could deviate from those of BFM when the emission reduction ratio was higher than 50 %. Under multi-source control scenarios, the results of source contribution analyses obtained from RSM-DM and OSAT were reasonably well, but the performance of OSAT was limited by its capability in representing the nonlinearity of O₃ response to emission reductions of its precursors, particularly NO_x. The results of this pilot study in the PRD showed that the RSM-DM appeared to replicate the nonlinearity of O₃ chemistry reasonably well (e.g., O₃ disbenefits due to local NO_x emission reductions in Guangzhou city). Based on the source contribution results, on-road mobile (including both NO_x and VOC emissions) and industrial process (mainly VOC emissions) sources were identified as two major contribution sectors by both RSM-DM and OSAT, contributing an average of 31.5 % and 11.4 % (estimated by RSM-DM) and 29.2 % and 13.0 % (estimated by OSAT) respectively to O₃ formation in 9 cities of the PRD. Therefore, the reinforced emission reductions on NO_x and VOC from on-road mobile and industrial process sources in the central cities (i.e., Guangzhou, Foshan, Dongguan, Shenzhen, and Zhongshan) were suggested to effectively mitigate the ambient O₃ levels in the PRD.

Benefits of near-zero freight: The air quality and health impacts of low-NOx compressed natural gas trucks

The use of low-NOx compressed natural gas (CNG) medium-duty vehicles (MDVs) and heavy-duty vehicles (HDVs) has the potential to significantly reduce NO_x emissions and yield improvements in regional air quality. However, the extent of air quality improvement depends on many factors including future levels of vehicle deployment, the evolution of emissions from other sources, and meteorology. An analysis of the impacts requires modeling the atmosphere to account for both primary and secondary air pollutants, and the use of health impact assessment tools to map air quality changes into quantifiable metrics of human health. Here, we quantify and compare the air quality and health impacts associated with the deployment of low-NO_x CNG engines to power future MDV and HDV fleets in California relative to both a business-as-usual and a more advanced fleet composition. The results project that reductions in summer ground-level ozone could reach 13 ppb when compared to a baseline fleet of diesel and gasoline HDV and MDV and could reach 6 ppb when compared to a cleaner fleet that includes some zero-emission vehicles and fuels. Similarly, for all CNG cases considered reductions in PM_2.5 are predicted to range from 1.2 ug/m³ to 2.7 ug/m³ for a summer episode and from 3.1 ug/m³ to approximately 7.8 ug/m³ for a winter episode. These improvements yield short-term health benefits equivalent to $47 to $56 million in summer and $38 to $43 million in winter during episodes conducive to poor air quality. Additionally, the use of zero emission vehicle options such as battery electric and hydrogen fuel cell trucks could achieve approximately 25% to 31% higher benefits for an equivalent fleet penetration level due to the additional emission reductions achieved.Implications: The paper provides a quantitative estimate of the air quality and human health benefits that can be achieved through the use of novel compressed natural gas engines (i.e., low-NO_x CNG) in medium- and heavy-duty vehicles and provide a comparison with zero emission vehicles. Thus, our findings will provide support for policy development seeking to transform the trucking sector to meet clean air and climate goals given the current struggle policymakers have with selecting between alternative truck technologies due to variance in factors like cost and technical maturity.

Health Benefits in California of Strengthening the Fine Particulate Matter Standards

The Clean Air Act requires the United States Environmental Protection Agency to review routinely the National Ambient Air Quality Standards, including fine particulate matter (PM_2.5). A non-governmental Independent Particulate Matter Review Panel recently concluded that the current PM_2.5 standards do not protect public health adequately and recommended revising the daily standard from 35 to 25-30 μg/m³ and the annual standard from 12 to 8-10 μg/m³. To assess the public health implications of adopting the PM_2.5 standards proposed by the panel, the health benefits are quantified from their implementation based on both current (observed) and future (simulated) air quality data for California. The findings indicate that strengthening the standards would provide significant public health benefits valued at $42-$149 billion. Additionally, the stronger standards are shown to benefit environmental justice via health savings that are allocated more within environmentally and socioeconomically disadvantaged communities.

The Chesapeake Bay Program Modeling System: Overview and Recommendations for Future Development

The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.

Tempo-spatial patterns of PM2.5 measured using a portable particulate monitor around a mine complex in Canada's Arctic

Mining activities in Canada's pristine Arctic (e.g., driving on unpacked roads, blasts, rock grinding, diesel combustion, and garbage incineration) could add local sources of airborne fine particulate matter with a diameter of < 2.5 μm (PM_2.5) to their surrounding area. The increase in PM_2.5 above the background level around a mine represents a potential disturbance to caribou. To quantify the spatial distribution of the elevated PM_2.5, we investigated three different sampling schemes to measure PM_2.5 concentration using a portable monitor. We found that the best sampling scheme was to use the regional background PM_2.5 as the reference and analyze the anomaly of PM_2.5 measured at sites around the mine complex from the background level. The regional background PM_2.5 values were measured at the Daring Lake Tundra Research Station during 2018 and 2019. Our results indicated that the background PM_2.5 was not a low and constant value but varied with rain events, wind direction, and the impacts of forest fire smoke. After excluding periods affected by forest fires smokes, we found the background PM_2.5 was close to 0 μg m^-3 for the first few hours after rain, and then increased logistically with the time after rain (t_ar) to the maximum of 5 (or 10) μg m^-3 when the wind came from the north (or south) of the NW-SE axis. The NW-SE axis in western Canada divides the tundra north with few anthropogenic PM_2.5 sources from the forested south with many PM_2.5 sources from forest fire smokes and human activities. Analyses of PM_2.5 anomaly from the background (i.e., PM_2.5 measured at a site around the mining complex-the background level at the corresponding t_ar and wind direction) revealed that the zone of elevated PM_2.5 around the mine (Z_epm) expanded with t_ar. In the first few hours after rain, PM_2.5 was close to 0 everywhere except within meters of a source (e.g., a truck exhaust) in the downwind direction. During t_ar = 6 to 96 h, Z_epm expanded to 6.3 km in the downwind direction when the wind came from south of the NW-SE axis. A similar result was found in the downwind direction when the wind came from north of the NW-SE axis, with Z_epm = 4.4 km. In the upwind direction, the value of Z_epm was much smaller, being 0.7 km (or 1.0 km) when the wind came from the north (or south) of the NW-SE axis. For the period of t_ar between 96 and 192 hours, Z_epm further expanded to 21.2 km when the wind from the south of the NW-SE axis. The results from this study indicated that this reference paradigm that uses the regional background PM_2.5 as the reference in combination with a portable PM_2.5 monitor worked well for quantifying the tempo-spatial patterns of PM_2.5 at locations in remote and mostly pristine Arctic. However, their effectiveness for other regions needs further investigation.

Measurement of heterogeneous uptake of NO2 on inorganic particles, sea water and urban grime

Heterogeneous reactions of NO₂ on different surfaces play an important role in atmospheric NO_x removal and HONO formation, having profound impacts on photochemistry in polluted urban areas. Previous studies have suggested that the NO₂ uptake on the ground or aerosol surfaces could be a dominant source for elevated HONO during the daytime. However, the uptake behavior of NO₂ varies with different surfaces, and different uptake coefficients were used or derived in different studies. To obtain a more holistic picture of heterogeneous NO₂ uptake on different surfaces, a series of laboratory experiments using different flow tube reactors was conducted, and the NO₂ uptake coefficients (γ) were determined on inorganic particles, sea water and urban grime. The results showed that heterogeneous reactions on those surfaces were generally weak in dark conditions, with the measured γ varied from <10^-8 to 3.2 × 10^-7 under different humidity. A photo-enhanced uptake of NO₂ on urban grime was observed, with the obvious formation of HONO and NO from the heterogeneous reaction. The photo-enhanced γ was measured to be 1.9 × 10^-6 at 5% relative humidity (RH) and 5.8 × 10^-6 at 70% RH on urban grime, showing a positive RH dependence for both NO₂ uptake and HONO formation. The results demonstrate an important role of urban grime in the daytime NO₂-to-HONO conversion, and could be helpful to explain the unknown daytime HONO source in the polluted urban area.

Synergistic aircraft and ground observations of transported wildfire smoke and its impact on air quality in New York City during the summer 2018 LISTOS campaign

Air pollution associated with wildfire smoke transport during the summer can significantly affect ozone (O₃) and particulate matter (PM) concentrations, even in heavily populated areas like New York City (NYC). Here, we use observations from aircraft, ground-based lidar, in-situ analyzers and satellite to study and assess wildfire smoke transport, vertical distribution, optical properties, and potential impact on air quality in the NYC urban and coastal areas during the summer 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We investigate an episode of dense smoke transported and mixed into the planetary boundary layer (PBL) on August 15-17, 2018. The horizontal advection of the smoke is shown to be characterized with the prevailing northwest winds in the PBL (velocity > 10 m/s) based on Doppler wind lidar measurements. The wildfire sources and smoke transport paths from the northwest US/Canada to northeast US are identified from the NOAA hazard mapping system (HMS) fires and smoke product and NOAA-HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory analysis. The smoke particles are distinguished from the urban aerosols by showing larger lidar-ratio (70-sr at 532-nm) and smaller depolarization ratio (0.02) at 1064-nm using the NASA High Altitude Lidar Observatory (HALO) airborne high-spectral resolution lidar (HSRL) measurements. The extinction-related angstrom exponents in the near-infrared (IR at 1020-1640 nm) and Ultraviolet (UV at 340-440 nm) from NASA-Aerosol Robotic Network (AERONET) product show a reverse variation trend along the smoke loadings, and their absolute differences indicate strong correlation with the smoke-Aerosol Optical Depth (AOD) (R > 0.94). We show that the aloft smoke plumes can contribute as much as 60-70% to the column AOD and that concurrent high-loadings of O₃, carbon monoxide (CO), and black carbon (BC) were found in the elevated smoke layers from the University of Maryland (UMD) aircraft in-situ observations. Meanwhile, the surface PM_2.5 (PM with diameter ≤ 2.5 μm), organic carbon (OC) and CO measurements show coincident and sharp increase (e.g., PM_2.5 from 5 μg/m³ before the plume intrusion to ~30 μg/m³) with the onset of the plume intrusions into the PBL along with hourly O₃ exceedances in the NYC region. We further evaluate the NOAA-National Air Quality Forecasting Capability (NAQFC) model PBL-height, PM_2.5, and O₃ with the observations and demonstrate good consistency near the ground during the convective PBL period, but significant bias at other times. The aloft smoke layers are sometimes missed by the model.

The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2

Air quality modeling for research and regulatory applications often involves executing many emissions sensitivity cases to quantify impacts of hypothetical scenarios, estimate source contributions, or quantify uncertainties. Despite the prevalence of this task, conventional approaches for perturbing emissions in chemical transport models like the Community Multiscale Air Quality (CMAQ) model require extensive offline creation and finalization of alternative emissions input files. This workflow is often time-consuming, error-prone, inconsistent among model users, difficult to document, and dependent on increased hard disk resources. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, a component of CMAQv5.3 and beyond, addresses these limitations by performing these modifications online during the air quality simulation. Further, the model contains an Emission Control Interface which allows users to prescribe both simple and highly complex emissions scaling operations with control over individual or multiple chemical species, emissions sources, and spatial areas of interest. DESID further enhances the transparency of its operations with extensive error-checking and optional gridded output of processed emission fields. These new features are of high value to many air quality applications including routine perturbation studies, atmospheric chemistry research, and coupling with external models (e.g., energy system models, reduced-form models).

The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2

Air quality modeling for research and regulatory applications often involves executing many emissions sensitivity cases to quantify impacts of hypothetical scenarios, estimate source contributions, or quantify uncertainties. Despite the prevalence of this task, conventional approaches for perturbing emissions in chemical transport models like the Community Multiscale Air Quality (CMAQ) model require extensive offline creation and finalization of alternative emissions input files. This workflow is often time-consuming, error-prone, inconsistent among model users, difficult to document, and dependent on increased hard disk resources. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, a component of CMAQv5.3 and beyond, addresses these limitations by performing these modifications online during the air quality simulation. Further, the model contains an Emission Control Interface which allows users to prescribe both simple and highly complex emissions scaling operations with control over individual or multiple chemical species, emissions sources, and spatial areas of interest. DESID further enhances the transparency of its operations with extensive error-checking and optional gridded output of processed emission fields. These new features are of high value to many air quality applications including routine perturbation studies, atmospheric chemistry research, and coupling with external models (e.g., energy system models, reduced-form models).

COVID-19-Induced Lockdowns Indicate the Short-Term Control Effect of Air Pollutant Emission in 174 Cities in China

The contradiction between the regional imbalance and an one-size-fits-all policy is one of the biggest challenges in current air pollution control in China. With the recent implementation of first-level public health emergency response (FLPHER) in response to the COVID-19 pandemic in China (a total of 77 041 confirmed cases by February 22, 2020), human activities were extremely decreased nationwide and almost all economic activities were suspended. Here, we show that this scenario represents an unprecedented "base period" to probe the short-term emission control effect of air pollution at a city level. We quantify the FLPHER-induced changes of NO₂, SO₂, PM_2.5, and PM₁₀ levels in 174 cities in China. A machine learning prediction model for air pollution is established by coupling a generalized additive model, random effects meta-analysis, and weather research and forecasting model with chemistry analysis. The short-term control effect under the current energy structure in each city is estimated by comparing the predicted and observed results during the FLPHER period. We found that the short-term emission control effect ranges within 53.0%-98.3% for all cities, and southern cities show a significantly stronger effect than northern cities (P < 0.01). Compared with megacities, small-medium cities show a similar control effect on NO₂ and SO₂ but a larger effect on PM_2.5 and PM₁₀.

The contribution of wildland fire emissions to deposition in the U S: implications for tree growth and survival in the Northwest

Ecosystems require access to key nutrients like nitrogen (N) and sulfur (S) to sustain growth and healthy function. However, excessive deposition can also damage ecosystems through nutrient imbalances, leading to changes in productivity and shifts in ecosystem structure. While wildland fires are a known source of atmospheric N and S, little has been done to examine the implications of wildland fire deposition for vulnerable ecosystems. We combine wildland fire emission estimates, atmospheric chemistry modeling, and forest inventory data to (a) quantify the contribution of wildland fire emissions to N and S deposition across the U S, and (b) assess the subsequent impacts on tree growth and survival rates in areas where impacts are likely meaningful based on the relative contribution of fire to total deposition. We estimate that wildland fires contributed 0.2 kg N ha-1 yr-1 and 0.04 kg S ha-1 yr-1 on average across the U S during 2008-2012, with maxima up to 1.4 kg N ha-1 yr-1 and 0.6 kg S ha-1 yr-1 in the Northwest representing over ~30% of total deposition in some areas. Based on these fluxes, exceedances of S critical loads as a result of wildland fires are minimal, but exceedances for N may affect the survival and growth rates of 16 tree species across 4.2 million hectares, with the most concentrated impacts occurring in Oregon, northern California, and Idaho. Understanding the broader environmental impacts of wildland fires in the U S will inform future decision making related to both fire management and ecosystem services conservation.

Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability

Air pollution is a complex process and is affected by meteorological conditions and other chemical components. Numerous studies have demonstrated that data-driven spatio-temporal prediction models of PM2.5 concentration are comparable with the model-driven model. However, data-driven models are usually depending on the statistical correlation between PM2.5 and other factors and have challenges in dealing with causality in complex systems. In this paper, we argue that domain knowledge should be incorporated into data-driven models to enhance prediction accuracy and make the model more physically realistic. We focus on the influence of dynamic wind-field on PM2.5 concentration distribution and fuse the pollution diffusion distance with the deep learning model based on a wind-field surface. In order to model spatial dependence between monitoring stations, which is dynamic and anisotropic because of the wind-field, we proposed a hybrid deep learning framework, dynamic directed spatio-temporal graph convolution networks (DD-STGCN). It expanded the ability to deal with space-time prediction in the continuous and dynamic wind-field. We used a directed graph time-series to describe the vertex state and topological relationship between vertices and replaced traditional Euclidean distance with wind-field diffusion distance to describe the proximity relationship between vertices. Our experiment results demonstrated that the DD-STGCN model achieved a better prediction ability than LSTM, GC-LSTM, and STGCN models. Compared to the best comparison model, MAPE, MAE, and RMSE were improved by 10.2%, 9.7%, and 9.6% in 12 h on an average, respectively. The performance of our model was further tested during a haze period. In the case that two models both considered the effect of wind, compared with the pure data-driven model, our model performed better in prediction distribution and showed the benefit of spatial interpretability provided by domain knowledge.

Contribution of biogenic sources to secondary organic aerosol in the summertime in Shaanxi, China

A revised Community Multi-scale Air Quality (CMAQ) model with updated secondary organic aerosol (SOA) yields and a more detailed description of SOA formation from isoprene (ISOP) oxidation was applied to study the spatial distribution of SOA, its components and precursors in Shaanxi in July of 2013. The emissions of biogenic volatile organic compounds (BVOCs) were generated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN), of which ISOP and monoterpene (MONO) were the top two, with 1.73 × 10⁹ mol and 1.82 × 10⁸ mol, respectively. The spatial distribution of BVOCs emission was significantly correlated with the vegetation coverage distribution. ISOP and its intermediate semi-volatile gases were up to ∼7.0 and ∼1.4 ppb respectively in the ambient. SOA was generally 2-6 μg/m³, of which biogenic SOA (BSOA) accounted for as high as 84% on average. There were three main BVOCs Precursors including ISOP (58%) and MONO (8%) emit in the studied domain, and ISOP (9%) transported. The Guanzhong Plain had the highest BSOA concentrations of 3-5 μg/m³, and the North Shaanxi had the lowest of 2-3 μg/m³. More than half of BSOA was due to reactive surface uptake of ISOP epoxide (0.2-0.7 μg/m³, ∼19%), glyoxal (GLY) (0.2-0.5 μg/m³, ∼11%) and methylglyoxal (MGLY) (0.4-1.4 μg/m³, ∼32%), while the remaining was due to the traditional equilibrium partitioning of semi-volatile components (0.1-1.2 μg/m³, ∼25%) and oligomerization (0.2-0.4 μg/m³, ∼12%). Overall, SOA formed from ISOP contributed 1-3 μg/m³ (∼80%) to BSOA.

Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China

The absence of motor vehicle traffic and suspended manufacturing during the coronavirus disease 2019 (COVID-19) pandemic in China enabled assessment of the efficiency of air pollution mitigation. Up to 90% reduction of certain emissions during the city-lockdown period can be identified from satellite and ground-based observations. Unexpectedly, extreme particulate matter levels simultaneously occurred in northern China. Our synergistic observation analyses and model simulations show that anomalously high humidity promoted aerosol heterogeneous chemistry, along with stagnant airflow and uninterrupted emissions from power plants and petrochemical facilities, contributing to severe haze formation. Also, because of nonlinear production chemistry and titration of ozone in winter, reduced nitrogen oxides resulted in ozone enhancement in urban areas, further increasing the atmospheric oxidizing capacity and facilitating secondary aerosol formation.

Assessing PM2.5 emissions in 2020: The impacts of integrated emission control policies in China

Problems with PM_2.5 pollution in the Pearl River Delta (PRD) have been significantly reduced since the Chinese government released a series of emission control policies including the strengthened controls in the 13^th Five-Year Plan. This study assessed the efficacy of emission control measures using the Community Multiscale Air Quality (CMAQ) model to provide data-driven support to government decision making, which is becoming increasingly important. This study aimed to quantitatively evaluate the integrated results of proposed policies for controlling PM_2.5 concentrations. Accordingly, the regional 2015 emission inventory was modified with recently released government data for the PRD, and scenarios for four dynamical emission-reduction policies (S1-S4) were explored. The results show that all four proposed control measures can help to reduce PM_2.5 concentrations throughout Hong Kong (HK), Macao, and the PRD economic zone (PRD EZ) by 2020. In all cases, reductions in PM_2.5 concentrations were larger over PRD EZ than over HK. For HK, the predicted annual concentrations of PM_2.5 were less than 20 μg/m³ for S1-S3 and less than 15 μg/m³ for S4. For Macao, the predicted annual concentrations of PM_2.5 were less than 25 μg/m³ for S1 and less than 15 μg/m³ up to S3. Regionally, HK had the lowest PM_2.5 levels, and the area around Foshan had the highest. Controlling the sources of air pollution (i.e., industry, transport, power production, and other sources) within PRD can get most of the PRD EZ region to below 35 μg/m³. Similar national air quality management efforts could reduce PM_2.5 levels to less than 25 μg/m³ in the PRD EZ and less than 15 μg/m³ in HK. Control measures in S1 led to significant improvement in Shenzhen and HK, but the S3 option brought the greatest improvement for PRD EZ and Macao. The S4 policy option led to substantial reductions, particularly for HK.

A multiphase CMAQ version 5.0 adjoint

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US.

Synergistic Association of House Endotoxin Exposure and Ambient Air Pollution with Asthma Outcomes

Rationale: House endotoxin and ambient air pollution are risk factors for asthma; however, the effects of their coexposure on asthma are not well characterized.Objectives: To examine potential synergistic associations of coexposure to house dust endotoxin and ambient air pollutants with asthma outcomes.Methods: We analyzed data of 6,488 participants in the National Health and Nutrition Examination Survey 2005-2006. Dust from bedding and bedroom floor was analyzed for endotoxin content. The Community Multiscale Air Quality Modeling System (CMAQ) and Downscaler Model data were used to determine annual average particulate matter ≤2.5 μm in aerodynamic diameter (PM_2.5), ozone (O₃), and nitrogen dioxide (NO₂) exposures at participants' residential locations. The associations of the coexposures with asthma outcomes were assessed and tested for synergistic interaction.Measurements and Main Results: In adjusted analysis, PM_2.5 (CMAQ) (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.07-1.18), O₃ (Downscaler Model) (OR, 1.07; 95% CI, 1.02-1.13), and log₁₀ NO₂ (CMAQ) (OR, 3.15; 95% CI, 1.33-7.45) were positively associated with emergency room visits for asthma in the past 12 months. Coexposure to elevated concentrations of house dust endotoxin and PM_2.5 (CMAQ) was synergistically associated with the outcome, increasing the odds by fivefold (OR, 5.01; 95% CI, 2.54-9.87). A synergistic association was also found for coexposure to higher concentrations of endotoxin and NO₂ in children (OR, 3.45; 95% CI, 1.65-7.18).Conclusions: Coexposure to elevated concentrations of residential endotoxin and ambient PM_2.5 in all participants and NO₂ in children is synergistically associated with increased emergency room visits for asthma. Therefore, decreasing exposure to both endotoxin and air pollution may help reduce asthma morbidity.

Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland

The fine particulate matter baseline (PMB), which includes PM_2.5 monitor readings fused with Community Multiscale Air Quality (CMAQ) model predictions, using the Hierarchical Bayesian Model (HBM), is less accurate in rural areas without monitors. To address this issue, an upgraded HBM was used to form four experimental aerosol optical depth (AOD)-PM_2.5 concentration surfaces. A case-crossover design and conditional logistic regression evaluated the contribution of the AOD-PM_2.5 surfaces and PMB to four respiratory-cardiovascular hospital events in all 99 12 km² CMAQ grids, and in grids with and without ambient air monitors. For all four health outcomes, only two AOD-PM_2.5 surfaces, one not kriged (PMC) and the other kriged (PMCK), had significantly higher Odds Ratios (ORs) on lag days 0, 1, and 01 than PMB in all grids, and in grids without monitors. In grids with monitors, emergency department (ED) asthma PMCK on lag days 0, 1 and 01 and inpatient (IP) heart failure (HF) PMCK ORs on lag days 01 were significantly higher than PMB ORs. Warm season ORs were significantly higher than cold season ORs. Independent confirmation of these results should include AOD-PM_2.5 concentration surfaces with greater temporal-spatial resolution, now easily available from geostationary satellites, such as GOES-16 and GOES-17.

Impact of Reductions in Emissions from Major Source Sectors on Fine Particulate Matter-Related Cardiovascular Mortality

BACKGROUND

Reductions in ambient concentrations of fine particulate matter (PM2.5) have contributed to reductions in cardiovascular (CV) mortality.

OBJECTIVES

We examined changes in CV mortality attributed to reductions in emissions from mobile, point, areal, and nonroad sources through changes in concentrations of PM2.5 and its major components [nitrates, sulfates, elemental carbon (EC), and organic carbon (OC)] in 2,132 U.S. counties between 1990 and 2010.

METHODS

Using Community Multiscale Air Quality model estimated PM2.5 total and component concentrations, we calculated population-weighted annual averages for each county. We estimated PM2.5 total- and component-related CV mortality, adjusted for county-level population characteristics and baseline PM2.5 concentrations. Using the index of Emission Mitigation Efficiency for primary emission-to-particle pathways, we expressed changes in particle-related mortality in terms of precursor emissions by each sector.

RESULTS

PM2.5 reductions represented 5.7% of the overall decline in CV mortality. Large point source emissions of sulfur dioxide accounted for 6.685 [95% confidence interval (CI): 5.703, 7.667] fewer sulfate-related CV deaths per 100,000 people. Mobile source emissions of primary EC and nitrous oxides accounted for 3.396 (95% CI: 2.772, 4.020) and 3.984 (95% CI: 2.472, 5.496) fewer CV deaths per 100,000 people respectively. Increased EC and OC emissions from areal sources increased carbon-related CV mortality by 0.788 (95% CI: -0.540, 2.116) and 0.245 (95% CI: -0.697, 1.187) CV deaths per 100,000 people.

DISCUSSION

In a nationwide epidemiological study of emission sector contribution to PM2.5-related mortality, we found that reductions in sulfur-dioxide emissions from large point sources and nitrates and EC emissions from mobile sources contributed the largest reduction in particle-related mortality rates respectively. https://doi.org/10.1289/EHP5692.

Impact of halogen chemistry on summertime air quality in coastal and continental Europe: application of the CMAQ model and implications for regulation

Halogen (Cl, Br, and I) chemistry has been reported to influence the formation of secondary air pollutants. Previous studies mostly focused on the impact of chlorine species on air quality over large spatial scales. Very little attention has been paid to the effect of the combined halogen chemistry on air quality over Europe and its implications for control policy. In the present study, we apply a widely used regional model, the Community Multiscale Air Quality Modeling System (CMAQ), incorporated with the latest halogen sources and chemistry, to simulate the abundance of halogen species over Europe and to examine the role of halogens in the formation of secondary air pollution. The results suggest that the CMAQ model is able to reproduce the level of O₃, NO₂, and halogen species over Europe. Chlorine chemistry slightly increases the levels of OH, HO₂, NO₃, O₃, and NO₂ and substantially enhances the level of the Cl radical. Combined halogen chemistry induces complex effects on OH (ranging from -0.023 to 0.030 pptv) and HO₂ (in the range of -3.7 to 0.73 pptv), significantly reduces the concentrations of NO₃ (as much as 20 pptv) and O₃ (as much as 10 ppbv), and decreases NO₂ in highly polluted regions (as much as 1.7 ppbv); it increases NO₂ (up to 0.20 ppbv) in other areas. The maximum effects of halogen chemistry occur over oceanic and coastal regions, but some noticeable impacts also occur over continental Europe. Halogen chemistry affects the number of days exceeding the European Union target threshold for the protection of human beings and vegetation from ambient O₃. In light of the significant impact of halogen chemistry on air quality, we recommend that halogen chemistry be considered for inclusion in air quality policy assessments, particularly in coastal cities.

Multivariate Air Pollution Prediction Modeling with partial Missingness

Missing observations from air pollution monitoring networks have posed a longstanding problem for health investigators of air pollution. Growing interest in mixtures of air pollutants has further complicated this problem, as many new challenges have arisen that require development of novel methods. The objective of this study is to develop a methodology for multivariate prediction of air pollution. We focus specifically on tackling different forms of missing data, such as: spatial (sparse sites), outcome (pollutants not measured at some sites), and temporal (varieties of interrupted time series). To address these challenges, we develop a novel multivariate fusion framework, which leverages the observed inter-pollutant correlation structure to reduce error in the simultaneous prediction of multiple air pollutants. Our joint fusion model employs predictions from the Environmental Protection Agency's Community Multiscale Air Quality (CMAQ) model along with spatio-temporal error terms. We have implemented our models on both simulated data and a case study in South Carolina for 8 pollutants over a 28-day period in June 2006. We found that our model, which uses a multivariate correlated error in a Bayesian framework, showed promising predictive accuracy particularly for gaseous pollutants.

Comparison of PM2.5 Chemical Components over East Asia Simulated by the WRF-Chem and WRF/CMAQ Models: On the Models’ Prediction Inconsistency

High levels of atmospheric concentration of PM2.5 (particulate matters less than 2.5 μm in size) are one of the most urgent societal issues over the East Asian countries. Air quality models have been used as an essential tool to predict spatial and temporal distribution of the PM2.5 and to support relevant policy making. This study aims to investigate the performance of high-fidelity air quality models in simulating surface PM2.5 chemical composition over the East Asia region in terms of a prediction consistency, which is a prerequisite for accurate air quality forecasts and reliable policy decision. The WRF-Chem (Weather Research and Forecasting-Chemistry) and WRF/CMAQ (Weather Research and Forecasting/Community Multiscale Air Quality modeling system) models were selected and uniquely configured for a one-month simulation by controlling surface emissions and meteorological processes (model options) to investigate the prediction consistency focusing the analyses on the effects of meteorological and chemical processes. The results showed that the surface PM2.5 chemical components simulated by both the models had significant inconsistencies over East Asia ranging fractional differences of 53% ± 30% despite the differences in emissions and meteorological fields were minimal. The models’ large inconsistencies in the surface PM2.5 concentration were attributed to the significant differences in each model’s chemical responses to the meteorological variables, which were identified from the multiple linear regression analyses. Our findings suggest that the significant models’ prediction inconsistencies should be considered with a great caution in the PM2.5 forecasts and policy support over the East Asian region.

Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014

Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO₂, SO_2, O₃, PM_2.5, PM₁₀, NO₃⁻, NH₄⁺, EC, OC, SO₄²⁻) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R² values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R² values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R² values of 0.4 or more for all pollutants except SO₂.

Detection of Strong NOX Emissions from Fine-scale Reconstruction of the OMI Tropospheric NO2 Product

Satellite-retrieved atmospheric NO2 column products have been widely used in assessing bottom-up NOX inventory emissions emitted from large cities, industrial facilities, and power plants. However, the satellite products fail to quantify strong NOX emissions emitted from the sources less than the satellite’s pixel size, with significantly underestimating their emission intensities (smoothing effect). The poor monitoring of the emissions makes it difficult to enforce pollution restriction regulations. This study reconstructs the tropospheric NO2 vertical column density (VCD) of the Ozone Monitoring Instrument (OMI)/Aura (13 × 24 km2 pixel resolution at nadir) over South Korea to a fine-scale product (grid resolution of 3 × 3 km2) using a conservative spatial downscaling method, and investigates the methodological fidelity in quantifying the major Korean area and point sources that are smaller than the satellite’s pixel size. Multiple high-fidelity air quality models of the Weather Research and Forecast-Chemistry (WRF-Chem) and the Weather Research and Forecast/Community Multiscale Air Quality modeling system (WRF/CMAQ) were used to investigate the downscaling uncertainty in a spatial-weight kernel estimate. The analysis results showed that the fine-scale reconstructed OMI NO2 VCD revealed the strong NOX emission sources with increasing the atmospheric NO2 column concentration and enhanced their spatial concentration gradients near the sources, which was accomplished by applying high-resolution modeled spatial-weight kernels to the original OMI NO2 product. The downscaling uncertainty of the reconstructed OMI NO2 product was inherent and estimated by 11.1% ± 10.6% at the whole grid cells over South Korea. The smoothing effect of the original OMI NO2 product was estimated by 31.7% ± 13.1% for the 6 urbanized area sources and 32.2% ± 17.1% for the 13 isolated point sources on an effective spatial resolution that is defined to reduce the downscaling uncertainty. Finally, it was found that the new reconstructed OMI NO2 product had a potential capability in quantifying NOX emission intensities of the isolated strong point sources with a good correlation of R = 0.87, whereas the original OMI NO2 product failed not only to identify the point sources, but also to quantify their emission intensities (R = 0.30). Our findings highlight a potential capability of the fine-scale reconstructed OMI NO2 product in detecting directly strong NOX emissions, and emphasize the inherent methodological uncertainty in interpreting the reconstructed satellite product at a high-resolution grid scale.

Differential Effect of Ambient Air Pollution Exposure on Risk of Gestational Hypertension and Preeclampsia

Although ambient air pollution may increase hypertension risk through endothelial damage and oxidative stress, evidence is inconsistent regarding its effect on hypertension in pregnancy. Prior research has evaluated a limited scope of pollution species and often not differentiated preeclampsia, which may have a placental origin, from gestational hypertension. Among 49 607 women with at least 2 singleton deliveries in the Eunice Kennedy Shriver National Institute of Child Health and Human Development Consecutive Pregnancies Study (2002-2010), we estimated criteria pollutant and volatile organic compound levels during pregnancy using Community Multiscale Air Quality models and abstracted gestational hypertension and preeclampsia diagnoses from medical records. Generalized estimating equations accounted for repeat pregnancies and adjusted for ambient temperature and maternal age, race/ethnicity, body mass index, smoking, alcohol, parity, insurance, marital status, and asthma. Air pollution levels were low to moderate (eg, median 41.6 ppb [interquartile range, 38.9-43.7 ppb] for ozone and 35.1 ppb [28.9-40.3 ppb] for nitrogen oxides). Higher levels of most criteria pollutants during preconception and the first trimester were associated with lower preeclampsia risk, while higher second-trimester levels were associated with greater gestational hypertension risk. For example, an interquartile increase in first-trimester carbon monoxide was associated with a relative risk of 0.88 (95% CI, 0.81-0.95) for preeclampsia and second-trimester carbon monoxide a relative risk of 1.14 (95% CI, 1.07-1.22) for gestational hypertension. Volatile organic compounds, conversely, were not associated with gestational hypertension but consistently associated with higher preeclampsia risk. These findings further suggest air pollution may affect the development of hypertension in pregnancy, although differing causes of preeclampsia and gestational hypertension may alter these relationships.

Mapping Modeled Exposure of Wildland Fire Smoke for Human Health Studies in California

Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-h average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007-2013) was 4.91 μg/m3. The average concentration of fire-PM2.5 in California by year was 1.22 μg/m3 (~25% of total PM2.5). The fire-PM2.5 daily mean was estimated at 4.40 μg/m3 in a high fire year (2008). Based on the model-derived fire-PM2.5 data, 97.4% of California's population lived in a county that experienced at least one episode of high smoke exposure ("smokewave") from 2007-2013. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 μg/m3 bias) and 2013 (1.6 μg/m3 bias) while underestimating for 2009 (-2.1 μg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts.

Ambient air pollution and fetal growth restriction: Physician diagnosis of fetal growth restriction versus population-based small-for-gestational age

BACKGROUND

Ambient air pollution may affect fetal growth restriction (FGR) through several mechanisms. However, prior studies of air pollution and small-for-gestational age (SGA), a common proxy for FGR, have reported inconsistent findings.

OBJECTIVE

We assessed air pollution in relation to physician-diagnosed FGR and population-based SGA in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Consecutive Pregnancy Study (2002-2010).

METHODS

Among 50,005 women (112,203 singleton births), FGR was captured from medical records and ICD-9 codes, and SGA determined by population standards for birthweight <10th, <5th and <3rd percentile. Community Multiscale Air Quality models estimated ambient levels of seven criteria pollutants for whole pregnancy, 3-months preconception, and 1st, 2nd and 3rd trimesters. Generalized estimating equations with robust standard errors accounted for interdependency of pregnancies within participant. Models adjusted for maternal age, race/ethnicity, pre-pregnancy body mass index, smoking, alcohol, parity, insurance, marital status, asthma and temperature.

RESULTS

FGR was diagnosed in 1.5% of infants, and 6.7% were <10th, 2.7% <5th and 1.5% <3rd percentile for SGA. Positive associations of SO2, NO2 and PM10 and negative associations of O3 with FGR were observed throughout preconception and pregnancy. For example, an interquartile increase in whole pregnancy SO2 was associated with 16% (95% CI 8%, 25%) increased FGR risk, 17% for NO2 (95% CI 9%, 26%) and 12% for PM10 (95% CI 6%, 19%). Associations with SGA were less clear.

CONCLUSIONS

Chronic exposure to air pollution may be associated with FGR but not SGA in this low-risk population.

Simulating lightning NO production in CMAQv5.2: performance evaluations

This study assesses the impact of the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model on ground-level air quality as well as aloft atmospheric chemistry through detailed evaluation of model predictions of nitrogen oxides (NO x ) and ozone (O3) with corresponding observations for the US. For ground-level evaluations, hourly O3 and NO x values from the U.S. EPA Air Quality System (AQS) monitoring network are used to assess the impact of different LNO schemes on model prediction of these species in time and space. Vertical evaluations are performed using ozonesonde and P-3B aircraft measurements during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign conducted in the Baltimore- Washington region during July 2011. The impact on wet deposition of nitrate is assessed using measurements from the National Atmospheric Deposition Program's National Trends Network (NADP NTN). Compared with the Base model (without LNO), the impact of LNO on surface O3 varies from region to region depending on the Base model conditions. Overall statistics suggest that for regions where surface O3 mixing ratios are already overestimated, the incorporation of additional NO from lightning generally increased model overestimation of mean daily maximum 8 h (DM8HR) O3 by 1-2 ppb. In regions where surface O3 is underestimated by the Base model, LNO can significantly reduce the underestimation and bring model predictions close to observations. Analysis of vertical profiles reveals that LNO can significantly improve the vertical structure of modeled O3 distributions by reducing underestimation aloft and to a lesser degree decreasing overestimation near the surface. Since the Base model underestimates the wet deposition of nitrate in most regions across the modeling domain with the exception of the Pacific Coast, the inclusion of LNO leads to reduction in biases and errors and an increase in correlation coefficients at almost all the NADP NTN sites. Among the three LNO schemes described in Kang et al. (2019), the hNLDN scheme, which is implemented using hourly observed lightning flash data from National Lightning Detection Network (NLDN), performs best for comparisons with ground-level values, vertical profiles, and wet deposition of nitrate; the mNLDN scheme (the monthly NLDN-based scheme) performed slightly better. However, when observed lightning flash data are not available, the linear regression-based parameterization scheme, pNLDN, provides an improved estimate for nitrate wet deposition compared to the base simulation that does not include LNO.

Future year ozone source attribution modeling study using CMAQ-ISAM

To achieve the current United States National Ambient Air Quality Standards (NAAQS) attainment level for ozone or particulate matter, current photochemical air quality models include tools to determine source apportionment and/or source sensitivity. Previous studies by the authors have used the Ozone and Particulate Matter Source Apportionment Technology and Higher-order Decoupled Direct Method probing tools in CAMx to investigate these source-receptor relationships for ozone. The recently available source apportionment for CMAQ, referred to as the Integrated Source Apportionment Method (ISAM), was used in this study to conduct future year (2030) source attribution modeling. The CMAQ-ISAM ozone source attribution results for selected cities across the U.S. showed boundary conditions were the dominant contributor to the future year highest July maximum daily 8-hour average (MDA8) ozone concentrations. Point sources were generally larger contributors in the eastern U.S. than in the western U.S. The contributions of on-road mobile emissions were around 5 ppb at most of the cities selected for analysis. Off-road mobile source contributions were around 20 ppb or nearly 30%. Since boundary conditions play an important role in future year ozone levels, it is important to characterize future year boundary conditions accurately. The current implementation of ISAM in CMAQ 5.0.2 requires significant computing resources for ozone source attribution, making it difficult to conduct long-term simulations for large domains. The computing requirements for PM source attribution are even more onerous. CMAQ 5.2 was released after this study was completed, and does not include ISAM. If an efficient version of ISAM becomes available, it could be used in long-term ozone and PM2.5 studies. Implications: Ozone source attribution results provide useful information on important emission source contribution categories and provide some initial guidance on future emission reduction strategies. This study explains a new source apportionment technique, CMAQ-ISAM, and compares it to CAMx OSAT. The techniques have similar results: ozone's highest source contributor is boundary conditions, followed by point sources, then off-road mobile sources. The current version of ISAM in CMAQ 5.0.2 requires significant computing resources for ozone source attribution, while the computing requirements for PM source attribution are even more onerous. CMAQ 5.2 was released after this study was completed, and does not include ISAM.

Future-Year Ozone Isopleths for South Coast, San Joaquin Valley, and Maryland

Many areas of the United States are working toward achieving the 2015 ozone National Ambient Air Quality Standard (NAAQS) attainment level. The objective of this study was to develop future-year (2030) volatile organic compounds and nitrogen oxides (VOC-NOx) isopleth diagrams of the 4th highest maximum daily 8-h average ozone design value concentrations at monitors of interest in the South Coast Air Basin (SoCAB) and San Joaquin Valley (SJV) in California, and in Maryland. The simulation results showed there would be attainment of the 2015 ozone NAAQS in 2030 without further controls at the selected monitors: 27% in SoCAB, 57% in SJV, and 100% in Maryland. The SoCAB ozone isopleths developed in this study were compared with those reported in the South Coast Air Quality Management District 2016 Air Quality Management Plan. There are several differences between the two modeling studies, the results are qualitatively similar for most of the monitors in the relative amounts of additional emission reductions needed to achieve the ozone NAAQS. The results of this study provide insight into designing potential control strategies for ozone attainment in future years for areas currently in non-attainment. Additional photochemical modeling using these strategies can then provide confirmation of the effectiveness of the controls.

Air-quality-related health impacts from climate change and from adaptation of cooling demand for buildings in the eastern United States: An interdisciplinary modeling study

BACKGROUND

Climate change negatively impacts human health through heat stress and exposure to worsened air pollution, amongst other pathways. Indoor use of air conditioning can be an effective strategy to reduce heat exposure. However, increased air conditioning use increases emissions of air pollutants from power plants, in turn worsening air quality and human health impacts. We used an interdisciplinary linked model system to quantify the impacts of heat-driven adaptation through building cooling demand on air-quality-related health outcomes in a representative mid-century climate scenario.

METHODS AND FINDINGS

We used a modeling system that included downscaling historical and future climate data with the Weather Research and Forecasting (WRF) model, simulating building electricity demand using the Regional Building Energy Simulation System (RBESS), simulating power sector production and emissions using MyPower, simulating ambient air quality using the Community Multiscale Air Quality (CMAQ) model, and calculating the incidence of adverse health outcomes using the Environmental Benefits Mapping and Analysis Program (BenMAP). We performed simulations for a representative present-day climate scenario and 2 representative mid-century climate scenarios, with and without exacerbated power sector emissions from adaptation in building energy use. We find that by mid-century, climate change alone can increase fine particulate matter (PM2.5) concentrations by 58.6% (2.50 μg/m3) and ozone (O3) by 14.9% (8.06 parts per billion by volume [ppbv]) for the month of July. A larger change is found when comparing the present day to the combined impact of climate change and increased building energy use, where PM2.5 increases 61.1% (2.60 μg/m3) and O3 increases 15.9% (8.64 ppbv). Therefore, 3.8% of the total increase in PM2.5 and 6.7% of the total increase in O3 is attributable to adaptive behavior (extra air conditioning use). Health impacts assessment finds that for a mid-century climate change scenario (with adaptation), annual PM2.5-related adult mortality increases by 13,547 deaths (14 concentration-response functions with mean incidence range of 1,320 to 26,481, approximately US$126 billion cost) and annual O3-related adult mortality increases by 3,514 deaths (3 functions with mean incidence range of 2,175 to 4,920, approximately US$32.5 billion cost), calculated as a 3-month summer estimate based on July modeling. Air conditioning adaptation accounts for 654 (range of 87 to 1,245) of the PM2.5-related deaths (approximately US$6 billion cost, a 4.8% increase above climate change impacts alone) and 315 (range of 198 to 438) of the O3-related deaths (approximately US$3 billion cost, an 8.7% increase above climate change impacts alone). Limitations of this study include modeling only a single month, based on 1 model-year of future climate simulations. As a result, we do not project the future, but rather describe the potential damages from interactions arising between climate, energy use, and air quality.

CONCLUSIONS

This study examines the contribution of future air-pollution-related health damages that are caused by the power sector through heat-driven air conditioning adaptation in buildings. Results show that without intervention, approximately 5%-9% of exacerbated air-pollution-related mortality will be due to increases in power sector emissions from heat-driven building electricity demand. This analysis highlights the need for cleaner energy sources, energy efficiency, and energy conservation to meet our growing dependence on building cooling systems and simultaneously mitigate climate change.