The impact of the direct effect of aerosols on meteorology and air quality using aerosol optical depth assimilation during the KORUS-AQ campaign

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021)

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

Aerosol-radiation interaction and its variation in North China within 2015-2019 period under continuous PM2.5 improvements

A study was conducted on aerosol-radiation interactions over six cities in this region within the 2015-2019 period. WRF-Chem simulations on 2017 showed that based on the six-city average, the aerosol load (PM2.5 concentrations) of 121.9, 49.6, 43.3, and 66.3 µg/m3 in January, April, July, and October, mainly lowered the level of downward shortwave radiation by 38.9, 24.0, 59.1, and 24.4 W/m2 and reduced the boundary layer height by 79.9, 40.8, 87.4, and 31.0 m, via scattering and absorbing solar radiation. The sensitivity of meteorological changes to identical aerosol loads varied in the order July > January > October and April. Then, the cooling and stabilizing effects of aerosols further led to increases in PM2.5, by 23.0, 3.4, 4.6, and 7.3 µg/m3 respectively in the four months. The sensitivity of the effect of aerosols on PM2.5 was greatest in January rather than in July, contrary to the effect on meteorology. Moreover, a negative linear relation was observed between daily BLH reductions and aerosol loads in fall and winter, and between PM2.5 increases and aerosol loads in all seasons. With the PM2.5 pollution improvements in this region, the aerosol radiative forcing was effectively reduced. This should result in daily BLH increases of 10-24 m in fall and winter, and the estimates in Beijing agreed well with the corresponding results based on AMDAR data. Additionally, the reduction in aerosol radiation effects brought about daily PM2.5 decreases of 1.6-2.8 µg/m3, accounting for 7.0%-17.7% in PM2.5 improvements.

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India

The spatial coverage of PM2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM2.5. MERRA-2 does not explicitly measure PM2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM2.5 was observed over many monitoring stations ranging from -20 to 60 μg m-3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = -0.03 μg m-3), followed by the northern region (r2 = 0.63 and MB = -0.10 μg m-3), which showed better output due to the frequent observation of PM2.5 >100 μg m-3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = -0.60 μg m-3). Overall, India's PM2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique.

Assessment of urban sprawls, amenities, and indifferences of LST and AOD in sub-urban area: a case study of Jammu

Urbanization, particularly in peri-urban areas, often results in critically transforming the regional land use and land cover (LULC). The increased built-up in peri-urban areas affects the regional accessibility of residents of urban clusters to requisite amenities and severely affects the regional environment, as observed in the case of Jammu district situated in the foothills of the Indian Himalayas. The present study is aimed at assessing the rise of urban sprawls in Jammu district over the past two decades and how the urbanization has affected the lag in the number of amenities corresponding to the urban growth based on qualitative parameters. Further, a parameterization scheme is developed to assess the amenities quality. A comparison is made with Indore, a planned smart city, to assess the status of urbanization and residential quality based on an amenity index. The study also investigates the indifferences observed in some of the climate variables in the urban and sub-urban settings of the Jammu district. The investigation is conducted through a multi-ring buffer analysis approach utilizing the land use land cover (LULC) products based on Landsat 8/7 satellite imagery of 2002, 2013, and 2021. The indifferences in the settings are analyzed using MODIS aerosol optical depth (AOD) and land surface temperature (LST) products. The analysis leads to determination of critical urban parameters including the urban area, density, and growth rate, revealing significant urbanization at 25-27 km from the city center. Significant indifferences are observed in urban and sub-urban areas indicating higher rise in LST and AOD, particularly in the recent decade. These investigations provide critical information to urban and climate solution authorities for planning and management, particularly in critically endangered areas.

A Deep Convolutional Neural Network Model for Improving WRF Simulations

Advancements in numerical weather prediction (NWP) models have accelerated, fostering a more comprehensive understanding of physical phenomena pertaining to the dynamics of weather and related computing resources. Despite these advancements, these models contain inherent biases due to parameterization of the physical processes and discretization of the differential equations that reduce simulation accuracy. In this work, we investigate the use of a computationally efficient deep learning (DL) method, the convolutional neural network (CNN), as a postprocessing technique that improves mesoscale Weather Research and Forecasting (WRF) one-day simulation (with a 1-h temporal resolution) outputs. Using the CNN architecture, we bias-correct several meteorological parameters calculated by the WRF model for all of 2018. We train the CNN model with a four-year history (2014-2017) to investigate the patterns in WRF biases and then reduce these biases in simulations for surface wind speed and direction, precipitation, relative humidity, surface pressure, dewpoint temperature, and surface temperature. The WRF data, with a spatial resolution of 27 km, cover South Korea. We obtain ground observations from the Korean Meteorological Administration station network for 93 weather station locations. The results indicate a noticeable improvement in WRF simulations in all station locations. The average of annual index of agreement for surface wind, precipitation, surface pressure, temperature, dewpoint temperature, and relative humidity of all stations is 0.85 (WRF:0.67), 0.62 (WRF:0.56), 0.91 (WRF:0.69), 0.99 (WRF:0.98), 0.98 (WRF:0.98), and 0.92 (WRF:0.87), respectively. While this study focuses on South Korea, the proposed approach can be applied for any measured weather parameters at any location.

Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook

Observations and numerical models are mainly used to investigate the spatiotemporal distribution and vertical structure characteristics of aerosols to understand aerosol pollution and its effects. However, the limitations of observations and the uncertainties of numerical models bias aerosol calculations and predictions. Data assimilation combines observations and numerical models to improve the accuracy of the initial, analytical fields of models and promote the development of atmospheric aerosol pollution research. Numerous studies have been conducted to integrate multi-source data, such as aerosol optical depth and aerosol extinction coefficient profile, into various chemical transport models using various data assimilation algorithms and have achieved good assimilation results. The definition of data assimilation and the main algorithms will be briefly presented, and the progress of aerosol assimilation according to two types of aerosol data, namely, aerosol optical depth and extinction coefficient, will be presented. The application of vertical aerosol data assimilation, as well as the future trends and challenges of aerosol data assimilation, will be further analysed.

Comprehensive study of aerosols properties over various terrain types

Aerosols are a crucial part of the climate system. Numerous factors, including aerosols, govern Earth's radiation balance. Different aerosols have distinct radiational effects on the earth system, and thus the slight change in their composition may lead to a drastic change in their radiative effects. Aerosols' chemical and physical properties also depend on generation processes, generation source, and geographical location. Significant spatio-temporal inconsistency is noticed in the distribution of aerosols. It makes it much difficult task to assess their radiative properties. We attempted to explore aerosol's optical properties and wavelength dependence over different locations. We have used AERONET (Aerosol Robotic Network) data over various stations (Kanpur, Jaipur, Gandhi College, Pune) with varying terrain properties in the Indian continent. We have studied the variation of different optical parameters: aerosol optical depth (AOD), single scattering albedo (SSA), and Angstrom exponent (α), and their wavelength dependence. This study indicated that Jaipur is the cleanest site, with dust aerosols as a primary aerosol. Though over Pune also aerosol concentration was relatively low but the anthropogenic aerosols contributed primarily over this site. Over the Indo-Gangetic Plain (IGP) sites, dust aerosols dominated the pre-monsoon season, while anthropogenic aerosols dominated the post-monsoon and winter seasons. The scatter plot of AOD with α gives the details of different aerosols (desert dust, continental aerosols, mixed aerosol, biomass burning aerosols, and sulfate aerosols) in the different seasons and places. This study provides an overview of aerosol properties, dominant aerosols in the aerosol system, and their seasonal and spectral variation.

The sensitivities of ozone and PM2.5 concentrations to the satellite-derived leaf area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM_2.5 concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM_2.5 concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from -56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM_2.5 concentrations over inland areas ranging from 0.27 ppbV to -7.17 ppbV and 0.89 μg/m³ to -2.65 μg/m³, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to -2.86 ppbV and 3.41 μg/m³ to -7.47 μg/m³. The decreases in inland ozone and PM_2.5 concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM_2.5 formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM_2.5 concentrations due to reduced reactions of PM_2.5 precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM_2.5 concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM_2.5 inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Seasonal Dependence of Aerosol Data Assimilation and Forecasting Using Satellite and Ground-Based Observations

This study examines the performance of a data assimilation and forecasting system that simultaneously assimilates satellite aerosol optical depth (AOD) and ground-based PM10 and PM2.5 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The data assimilation case for the surface PM10 and PM2.5 concentrations exhibits a higher consistency with the observed data by showing more correlation coefficients than the no-assimilation case. The data assimilation also shows beneficial impacts on the PM10 and PM2.5 forecasts for South Korea for up to 24 h from the updated initial condition. This study also finds deficiencies in data assimilation and forecasts, as the model shows a pronounced seasonal dependence of forecasting accuracy, on which the seasonal changes in regional atmospheric circulation patterns have a significant impact. In spring, the forecast accuracy decreases due to large uncertainties in natural dust transport from the continent by north-westerlies, while the model performs reasonably well in terms of anthropogenic emission and transport in winter. When the south-westerlies prevail in summer, the forecast accuracy increases with the overall reduction in ambient concentration. The forecasts also show significant accuracy degradation as the lead time increases because of systematic model biases. A simple statistical correction that adjusts the mean and variance of the forecast outputs to resemble those in the observed distribution can maintain the forecast skill at a practically useful level for lead times of more than a day. For a categorical forecast, the skill score of the data assimilation run increased by up to 37% compared to that of the case with no assimilation, and the skill score was further improved by 10% through bias correction.

Long-term spatiotemporal trends in aerosol optical depth and its relationship with enhanced vegetation index and meteorological parameters over South Asia

Satellite-based aerosol optical depth (AOD) is columnar light extinction by aerosol absorption and scattering and has become the most important variable for the assessment of the spatiotemporal distribution of aerosols at a regional and global level. In this paper, we have used AOD observations of multiangle imaging spectroradiometer (MISR) from September 2002 to May 2017, moderate resolution imaging spectroradiometer (MODIS) from September 2002 to December 2020, and sea-viewing wide field-of-view sensor (SeaWiFS) from September 2002 to December 2010 over South Asia. We have observed the association of AOD with enhanced vegetation index (EVI) and meteorological variables (temperature (TEMP), wind speed (WS), and relative humidity (RH)) acquired from Giovanni during the period September 2002-December 2020. The satellite observations of Terra-, MISR-, and SeaWiFS-AOD were also compared with Aqua-AOD. The findings show that AOD in eastern Pakistan is higher than in the western Pakistan due to increase in population density and biomass burning. Mean annual peak AOD (˃ 0.7) has been observed over the IGB region because of the significant increase in economical, industrial, and agricultural activities while AOD of ˃ 0.6 is observed over Bangladesh. The lowest mean annual AOD of ˂ 0.3 is observed over northeastern Afghanistan, western Nepal, and Bhutan whereas the AOD of 0.3 is seen over Sri Lanka. The highest seasonal mean AOD of 0.8 has been seen over Bihar, India, and AOD of ~ 0.7 is observed over Bangladesh while the lowest AOD is observed over Afghanistan, Sri Lanka, Nepal, and Bhutan during the winter season. However, the mean AOD over eastern Pakistan is maximum in both monsoon and post-monsoon season but relatively low in pre-monsoon and winter. The highest positive seasonal AOD anomalies were observed over South Asia in winter season followed by post-monsoon, pre-monsoon, and least being monsoon. The higher mean AOD anomaly value is found to be 0.2 over eastern Pakistan and western India. In northeastern Pakistan and central India, AOD and RH are positively correlated (r ˃ 0.54) while negatively correlated over Afghanistan, southwestern region of Pakistan, eastern India, Nepal, Bhutan, and Bangladesh. AOD is negatively correlated (r =  ~  - 0.3) with EVI over eastern Pakistan and western India. The highest correlation coefficient (r) obtained among Terra and Aqua is 0.97, MISR and Aqua is 0.93, and SeaWiFS and Aqua is 0.58 over South Asia.

Impact of Wildfires on Meteorology and Air Quality (PM2.5 and O3) over Western United States during September 2017

In this study, we investigated the impact of wildfires on meteorology and air quality (PM2.5 and O3) over the western United States during the September 2017 period. This is done by using Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate scenarios with wildfires (base case) and without wildfires (sensitivity case). Our analysis performed during the first half of September 2017 (when wildfire activity was more intense) reveals a reduction in modelled daytime average shortwave surface downward radiation especially in locations close to wildfires by up to 50 W m−2, thus resulting in the reduction of the diurnal average surface temperature by up to 0.5 °C and the planetary boundary layer height by up to 50 m. These changes are mainly attributed to aerosol-meteorology feedbacks that affect radiation and clouds. The model results also show mostly enhancements for diurnally averaged cloud optical depth (COD) by up to 10 units in the northern domain due to the wildfire-related air quality. These changes occur mostly in response to aerosol–cloud interactions. Analysis of the impact of wildfires on chemical species shows large changes in daily mean PM2.5 concentrations (exceeding by 200 μg m−3 in locations close to wildfires). The 24 h average surface ozone mixing ratios also increase in response to wildfires by up to 15 ppbv. The results show that the changes in PM2.5 and ozone occur not just due to wildfire emissions directly but also in response to changes in meteorology, indicating the importance of including aerosol-meteorology feedbacks, especially during poor air quality events.

Unraveling pathways of elevated ozone induced by the 2020 lockdown in Europe by an observationally constrained regional model using TROPOMI

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NO _x and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO₂ columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NO _x emissions in March 2020 (14 %-31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %-51 %) in April. However, NO _x emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO₂. This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO₂ reductions occurring in polluted areas are described fairly well by the model (model: -21 ± 17 %, observation: -29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r = 0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, + 1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by -4.83 ppbv, while ozone production rates dampened by largely negative J NO 2 [ NO 2 ] - k NO + O 3 [ NO ] [ O 3 ] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies.

A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry-meteorology feedbacks on air quality

The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry-meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O₃) and fine particulate matter (PM_2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O₃, sulfate, nitrate, ammonium, elemental carbon, tropospheric O₃ residual, and column nitrogen dioxide (NO₂). For example, the mean biases have been reduced by more than 10 W m^-2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O₃. However, relatively large biases still exist for cloud predictions, some PM_2.5 species, and PM₁₀ that warrant follow-up studies to better understand those issues. The impacts of chemistry-meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O₃, nitrogen oxide (NO _x ), volatile organic compounds (VOCs), and PM_2.5 by 3.1% (up to 27.8%), 4.2% (up to 16.2%), 6.6% (up to 50.9%), 5.8% (up to 46.6%), and 8.6% (up to 49.1%), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry-meteorology feedbacks on air quality

The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry-meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O₃) and fine particulate matter (PM_2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O₃, sulfate, nitrate, ammonium, elemental carbon, tropospheric O₃ residual, and column nitrogen dioxide (NO₂). For example, the mean biases have been reduced by more than 10 W m^-2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O₃. However, relatively large biases still exist for cloud predictions, some PM_2.5 species, and PM₁₀ that warrant follow-up studies to better understand those issues. The impacts of chemistry-meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O₃, nitrogen oxide (NO _x ), volatile organic compounds (VOCs), and PM_2.5 by 3.1% (up to 27.8%), 4.2% (up to 16.2%), 6.6% (up to 50.9%), 5.8% (up to 46.6%), and 8.6% (up to 49.1%), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance

Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.

Role of sea fog over the Yellow Sea on air quality with the direct effect of aerosols

In this study, we investigate the impact of sea fog over the Yellow Sea on air quality with the direct effect of aerosols for the entire year of 2016. Using the WRF-CMAQ two-way coupled model, we perform four model simulations with the up-to-date emission inventory over East Asia and dynamic chemical boundary conditions provided by hemispheric model simulations. During the spring of 2016, prevailing westerly winds and anticyclones caused the formation of a temperature inversion over the Yellow Sea, providing favorable conditions for the formation of fog. The inclusion of the direct effect of aerosols enhanced its strength. On foggy days, we find dominant changes of aerosols at an altitude of 150-200 m over the Yellow Sea resulted by the production through aqueous chemistry (~12.36% and ~3.08% increases in sulfate and ammonium) and loss via the wet deposition process (~-2.94% decrease in nitrate); we also find stronger wet deposition of all species occurring in PBL. Stagnant conditions associated with reduced air temperature caused by the direct effect of aerosols enhanced aerosol chemistry, especially in coastal regions, and it exceeded the loss of nitrate. The transport of air pollutants affected by sea fog extended to a much broader region. Our findings show that the Yellow Sea acts as not only a path of long-range transport but also as a sink and source of air pollutants. Further study should investigate changes in the impact of sea fog on air quality in conjunction with changes in the concentrations of aerosols and the climate.

Correlations between Meteorological Indicators, Air Quality and the COVID-19 Pandemic in 12 Cities across China

BACKGROUND

COVID-19 is a global pandemic. The purpose of this study is to explore correlations between the novel coronavirus (COVID-19) and meteorological indicators from cities across China.

METHODS

We collected daily data of the cumulative number of infected, recovered and death cases, and the meteorological indicators including average temperature, wind speed, relative humidity, precipitation and air quality index (AQI) from 12 cities in China during the period of Jan 23 to Feb 22, 2020. Correlation tests were chosen for data analysis.

RESULTS

The average temperature and AQI showed significant association with the mortality rate of COVID-19. The mortality rate was not correlated with wind speed, relative humidity or precipitation. Meanwhile, higher average temperatures and more precipitation were beneficial for the recovery rate of COVID-19, but the recovery rate was not correlated with wind speed, relative humidity or AQI.

CONCLUSIONS

Our study provides a new basis for correlations between COVID-19, meteorological indicators and air quality index, which can help authorities to combat COVID-19.