PM2.5 and ozone health impacts and disparities in New York City: sensitivity to spatial and temporal resolution

Southern California ozone exposure disparities under different emissions control strategies in a low-carbon future

Environmental justice (EJ) has emerged as a critical consideration when planning new air pollution control strategies. In this study we analyze how traditional ozone (O3) control strategies for the year 2050 will affect exposure disparities, defined as departures from the population average exposure, for O3 and oxides of nitrogen (NOx) in Southern California. Future air quality fields were simulated using a chemical transport model under five emission scenarios that explore a range of traditional controls that target the largest sources of precursor emissions using a novel O3 source apportionment technique but without considering exposure disparities. We find that traditional O3 control strategies reduce O3 exposure disparities by <1.6 % and reduce NO2 exposure disparities by <9 % in Southern California. For the Black and African residents living in the urban core of Los Angeles, the relative NO2 exposure disparities increase from +23.1 % to +66.2 % and O3 exposure disparities increase from -3.3 % to +0.1 % due to NOx emissions reductions mainly in outlying regions and the NOx-rich environment in the urban core. Additional analysis shows that complete elimination of NOx emissions from Los Angeles International Airport (LAX) would reduce the NO2 exposure disparities by up to 50 %, but there is currently no practical method to achieve this goal. The results of the current study highlight the challenge of simultaneously attaining O3 standards and reducing exposure disparities for O3 and NO2 in cities with NOx-rich urban cores. Reducing emissions by region may be a solution to this challenge.

Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States

BACKGROUND

Ambient nitrogen dioxide (NO 2 ) and fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) threaten public health in the US, and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants.

OBJECTIVES

Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study, we compare injustices in NO 2 - and PM 2.5 -attributable health burdens, considering NO 2 -attributable health impacts across the entire US; document changing disparities in these health burdens over time (2010-2019); and evaluate how more stringent air quality standards would reduce disparities in health impacts associated with these pollutants.

METHODS

Through a health impact assessment, we quantified census tract-level variations in health outcomes attributable to NO 2 and PM 2.5 using health impact functions that combine demographic data from the US Census Bureau; two spatially resolved pollutant datasets, which fuse satellite data with physical and statistical models; and epidemiologically derived relative risk estimates and incidence rates from the Global Burden of Disease study.

RESULTS

Despite overall decreases in the public health damages associated with NO 2 and PM 2.5 , racial and ethnic relative disparities in NO 2 -attributable pediatric asthma and PM 2.5 -attributable premature mortality have widened in the US during the last decade. Racial relative disparities in PM 2.5 -attributable premature mortality and NO 2 -attributable pediatric asthma have increased by 16% and 19%, respectively, between 2010 and 2019. Similarly, ethnic relative disparities in PM 2.5 -attributable premature mortality have increased by 40% and NO 2 -attributable pediatric asthma by 10%.

DISCUSSION

Enacting and attaining more stringent air quality standards for both pollutants could preferentially benefit the most marginalized and minoritized communities by greatly reducing racial and ethnic relative disparities in pollution-attributable health burdens in the US. Our methods provide a semi-observational approach to track changes in disparities in air pollution and associated health burdens across the US. https://doi.org/10.1289/EHP11900.

The effects of surface vegetation coverage on the spatial distribution of PM2.5 in the central area of Nanchang City, China

The frequent occurrence of haze has caused widespread concern in China, and PM2.5 is thought to be the main cause. Previous research showed that PM2.5 was not only influenced by meteorological conditions but also by land cover especially surface vegetation. It was concluded that PM2.5 concentration is significantly influenced by surface vegetation, but spatially how and in what manner are still unanswered. Taking the central area of Nanchang City, China, as a case, this study firstly applied land use regression (LUR) model to simulate the distribution of PM2.5 in 2020. Then, the dichotomous model was used to determine vegetation coverage. A statistical regression model was used to analyze the influence of vegetation cover on PM2.5 and the scale effects. The results showed that (1) vegetation coverage and PM2.5 concentration were both significantly negatively correlated at the spatial scales selected for this study. (2) The effect of vegetation coverage on PM2.5 varied with season and the 500 m had the best correlation. (3) The non-linear regression model fits better than the linear model, and the effect of vegetation coverage on PM2.5 was complex. (4) The effect of vegetation coverage on PM2.5 concentration was different with PM2.5 concentration level. The higher the PM2.5 concentration, the more pronounced the effect of vegetation coverage on it. This study proposed the idea and method of coupling vegetation coverage with PM2.5 concentration at the regional scale from gradient landscape's point of view and provided some references for mitigating PM2.5 pollution through optimizing urban vegetation patterns.

Assessing the health impacts of PM2.5 and ozone pollution and their comprehensive correlation in Chinese cities based on extended correlation coefficient

The coordinated control of PM_2.5 and ozone pollution is becoming more and more important in the current and next stage of Chinese environmental pollution control. Existing studies are unable to provide sufficient quantitative assessments of the correlation of PM_2.5 and ozone pollution to support the coordinated control of the two air pollutants. This study develops a systematic method to comprehensively assess the correlation between PM_2.5 and ozone pollution, including the evaluation of the impact of two air pollutants on human health and the extended correlation coefficient (ECC) for assessing the bivariate correlation index of PM_2.5-ozone pollution in Chinese cities. According to the latest studies on epidemiology conducted in China, we take cardiovascular and cerebrovascular diseases and respiratory diseases as the ozone pollution's health burden when evaluating the health impact of ozone pollution. The results show that the health impact of PM_2.5 in China decreases by 25.9 % from 2015 to 2021, while the health impact of ozone increases by 11.8 %. The ECC of 335 cities in China shows an increasing-decreasing trend but has generally increased from 2015 to 2021. The study provides important support for an in-depth understanding of the correlation and development trend of Chinese PM_2.5 and ozone pollution by classifying the comprehensive PM_2.5-ozone correlation performances of Chinese cities into four types. China or other countries will get better environmental benefits by implementing different coordinated management approaches for different correlative types of regions based on the assessment method in this study.

An Investigation on the Possible Application Areas of Low-Cost PM Sensors for Air Quality Monitoring

In recent years, the availability on the market of low-cost sensors (LCSs) and low-cost monitors (LCMs) for air quality monitoring has attracted the interest of scientists, communities, and professionals. Although the scientific community has raised concerns about their data quality, they are still considered a possible alternative to regulatory monitoring stations due to their cheapness, compactness, and lack of maintenance costs. Several studies have performed independent evaluations to investigate their performance, but a comparison of the results is difficult due to the different test conditions and metrics adopted. The U.S. Environmental Protection Agency (EPA) tried to provide a tool for assessing the possible uses of LCSs or LCMs by publishing guidelines to assign suitable application areas for each of them on the basis of the mean normalized bias (MNB) and coefficient of variance (CV) indicators. Until today, very few studies have analyzed LCS performance by referring to the EPA guidelines. This research aimed to understand the performance and the possible application areas of two PM sensor models (PMS5003 and SPS30) on the basis of the EPA guidelines. We computed the R², RMSE, MAE, MNB, CV, and other performance indicators and found that the coefficient of determination (R²) ranged from 0.55 to 0.61, while the root mean squared error (RMSE) ranged from 11.02 µg/m³ to 12.09 µg/m³. Moreover, the application of a correction factor to include the humidity effect produced an improvement in the performance of the PMS5003 sensor models. We also found that, based on the MNB and CV values, the EPA guidelines assigned the SPS30 sensors to the "informal information about the presence of the pollutant" application area (Tier I), while PMS5003 sensors were assigned to the "supplemental monitoring of regulatory networks" area (Tier III). Although the usefulness of the EPA guidelines is acknowledged, it appears that improvements are necessary to increase their effectiveness.

Ambient PM2.5 and O3 pollution and health impacts in Iranian megacity

The main objectives of this study were to (i) assess variation within fine particles (PM_2.5) and tropospheric ozone (O₃) time series in Khorramabad (Iran) between 2019 (before) and 2020 (during COVID-19 pandemic); (ii) assess relationship between PM_2.5 and O₃, the PM_2.5/O₃ ratio, and energy consumption; and (iii) estimate the health effects of exposure to ambient PM_2.5 and O₃. From hourly PM_2.5 and O₃ concentrations, we applied both linear-log and integrated exposure-response functions, city-specific relative risk, and baseline incidence values to estimate the health effects over time. A significant correlation was found between PM_2.5 and O₃ (r =-0.46 in 2019, r =-0.55 in 2020, p < 0.05). The number of premature deaths for all non-accidental causes (27.5 and 24.6), ischemic heart disease (7.3 and 6.3), chronic obstructive pulmonary disease (17 and 19.2), and lung cancer (9.2 and 6.25) attributed to ambient PM_2.5 exposure and for respiratory diseases (4.7 and 5.4) for exposure to O₃ above 10 µg m^-3 for people older than 30-year-old were obtained in 2019 and 2020. The number of years of life lost declined by 11.6% in 2020 and exposure to PM_2.5 reduced the life expectancy by 0.58 and 0.45 years, respectively in 2019 and 2020. Compared to 2019, the restrictive measures associated to COVID-19 pandemic led to reduction in PM_2.5 (-25.5%) and an increase of O₃ concentration (+ 8.0%) in Khorramabad.

Daily Satellite Observations of Nitrogen Dioxide Air Pollution Inequality in New York City, New York and Newark, New Jersey: Evaluation and Application

Urban air pollution disproportionately harms communities of color and low-income communities in the U.S. Intraurban nitrogen dioxide (NO₂) inequalities can be observed from space using the TROPOspheric Monitoring Instrument (TROPOMI). Past research has relied on time-averaged measurements, limiting our understanding of how neighborhood-level NO₂ inequalities co-vary with urban air quality and climate. Here, we use fine-scale (250 m × 250 m) airborne NO₂ remote sensing to demonstrate that daily TROPOMI observations resolve a major portion of census tract-scale NO₂ inequalities in the New York City-Newark urbanized area. Spatiotemporally coincident TROPOMI and airborne inequalities are well correlated (r = 0.82-0.97), with slopes of 0.82-1.05 for relative and 0.76-0.96 for absolute inequalities for different groups. We calculate daily TROPOMI NO₂ inequalities over May 2018-September 2021, reporting disparities of 25-38% with race, ethnicity, and/or household income. Mean daily inequalities agree with results based on TROPOMI measurements oversampled to 0.01° × 0.01° to within associated uncertainties. Individual and mean daily TROPOMI NO₂ inequalities are largely insensitive to pixel size, at least when pixels are smaller than ∼60 km², but are sensitive to low observational coverage. We statistically analyze daily NO₂ inequalities, presenting empirical evidence of the systematic overburdening of communities of color and low-income neighborhoods with polluting sources, regulatory ozone co-benefits, and worsened NO₂ inequalities and cumulative NO₂ and urban heat burdens with climate change.

Macro-Morphological Traits of Leaves for Urban Tree Selection for Air Pollution Biomonitoring: A Review

Urban trees provide different ecosystem benefits, such as improving air quality due to the retention of atmospheric particulate matter (PM) on their leaves. The main objective of this paper was to study, through a systematic literature review, the leaf macro-morphological traits (LMTs) most used for the selection of urban trees as air pollution biomonitors. A citation frequency index was used in scientific databases, where the importance associated with each variable was organized by quartiles (Q). The results suggest that the most biomonitored air pollutants by the LMTs of urban trees were PM between 1-100 µm (Q1 = 0.760), followed by O₃ (Q2 = 0.586), PM_2.5 (Q2 = 0.504), and PM₁₀ (Q3 = 0.423). PM was probably the most effective air pollutant for studying and evaluating urban air quality in the context of tree LMTs. PM_2.5 was the fraction most used in these studies. The LMTs most used for PM monitoring were leaf area (Q1) and specific leaf area (Q4). These LMTs were frequently used for their easy measurement and quantification. In urban areas, it was suggested that leaf area was directly related to the amount of PM retained on tree leaves. The PM retained on tree leaves was also used to study other f associated urban air pollutants associated (e.g., heavy metals and hydrocarbons).

Internal Exposure and Distribution of Airborne Fine Particles in the Human Body: Methodology, Current Understandings, and Research Needs

Exposure to airborne fine particles (PM_2.5, particulate matter with aerodynamic diameter <2.5 μm) severely threatens global human health. Understanding the distribution and processes of inhaled PM_2.5 in the human body is crucial to clarify the causal links between PM_2.5 pollution and diseases. In contrast to extensive research on the emission and formation of PM_2.5 in the ambient environment, reports about the occurrence and fate of PM_2.5 in humans are still limited, although many studies have focused on the exposure and adverse effects of PM_2.5 with animal models. It has been shown that PM_2.5, especially ultrafine particles (UFPs), have the potential to go across different biological barriers and translocate into different human organs (i.e., blood circulation, brain, heart, pleural cavity, and placenta). In this Perspective, we summarize the factors affecting the internal exposure of PM_2.5 and the relevant analytical methodology and review current knowledge about the exposure pathways and distribution of PM_2.5 in humans. We also discuss the research challenges and call for more studies on the identification and characterization of key toxic species of PM_2.5, quantification of internal exposure doses in the general population, and further clarification of translocation, metabolism, and clearance pathways of PM_2.5 in the human body. In this way, it is possible to develop toxicity-based air quality standards instead of the currently used mass-based standards.

Effect of Different Combustion Modes on the Performance of Hydrogen Internal Combustion Engines under Low Load

Detailed hydrogen–air chemical reaction mechanisms were coupled with the three-dimensional grids of an experimental hydrogen internal combustion engine (HICE) to establish a computational fluid dynamics (CFD) combustion model based on the CONVERGE software. The effects of different combustion modes on the combustion and emission characteristics of HICE under low load were studied. The simulation results showed that, with the increase in excess hydrogen, the equivalent combustion and excessive hydrogen combustion modes with medium-cooled exhaust gas recirculation (EGR) dilution could improve the intensity of the in-cylinder combustion of HICE, increase the peak values of pressure and temperature in the cylinder, and then improve the indicated thermal efficiency of HICE under low load. However, larger excessive hydrogen combustion could weaken the improvement in performance; therefore, the performance of HICE could be comprehensively improved by the adoption of excessive hydrogen combustion with a fuel–air ratio below 1.2 under low load. The obtained conclusions indicate the research disadvantages in the power and emission performances of HICE under low load, and they are of great significance for the performance optimization of HICE. Furthermore, a control strategy was proposed to improve the stability of HICE under low load.

Trends, Issues and Future Directions of Urban Health Impact Assessment Research: A Systematic Review and Bibliometric Analysis

Health impact assessment (HIA) has been regarded as an important means and tool for urban planning to promote public health and further promote the integration of health concept. This paper aimed to help scientifically to understand the current situation of urban HIA research, analyze its discipline co-occurrence, publication characteristics, partnership, influence, keyword co-occurrence, co-citation, and structural variation. Based on the ISI Web database, this paper used a bibliometric method to analyze 2215 articles related to urban HIA published from 2012 to 2021. We found that the main research directions in the field were Environmental Sciences and Public Environmental Occupational Health; China contributed most articles, the Tehran University of Medical Sciences was the most influential institution, Science of the Total Environment was the most influential journal, Yousefi M was the most influential author. The main hotspots include health risk assessment, source appointment, contamination, exposure, particulate matter, heavy metals and urban soils in 2012–2021; road dust, source apposition, polycyclic aromatic hydrocarbons, air pollution, urban topsoil and the north China plain were always hot research topics in 2012–2021, drinking water and water quality became research topics of great concern in 2017–2021. There were 25 articles with strong transformation potential during 2020–2021, but most papers carried out research on the health risk assessment of toxic elements in soil and dust. Finally, we also discussed the limitations of this paper and the direction of bibliometric analysis of urban HIA in the future.

Low- and Medium-Cost Sensors for Tropospheric Ozone Monitoring—Results of an Evaluation Study in Wrocław, Poland

The paper presents the results of a 1.5-year evaluation study of low- and medium-cost ozone sensors. The tests covered electrochemical sensors: SensoriC O3 3E 1 (City Technology) and semiconductor gas sensors: SM50 OZU (Aeroqual), SP3-61-00 (FIS) and MQ131 (Winsen). Three copies of each sensor were enclosed in a measurement box and placed near the reference analyser (MLU 400). In the case of SensoriC O3 3E 1 sensors, the R2 values for the 1-h data were above 0.90 for the first 9 months of deployment, but a performance deterioration was observed in the subsequent months (R2 ≈ 0.6), due to sensor ageing processes. High linear relationships were observed for the SM50 devices (R2 > 0.94), but some periodic data offsets were reported, making regular checking and recalibration necessary. Power-law functions were used in the case of SP3-61-00 (R2 = 0.6–0.7) and MQ131 (R2 = 0.4–0.7). Improvements in the fittings were observed for models that included temperature and relative humidity data. In the case of SP3-61-00, the R2 values increased to above 0.82, while for MQ131 they increased to above 0.86. The study also showed that the measurement uncertainty of tested sensors meets the EU Directive 2008/50/EC requirements for indicative measurements and, in some cases, even for fixed measurements.

Estimation of long-term and short-term health effects attributed to PM2.5 standard pollutants in the air of Ardabil (using Air Q + model)

Clean air is considered as a basic need for human health. However, air pollution is a significant threat to health in developed and developing countries. The aim of this study was to estimate the health effects attributed to PM_2.5 pollutants in the air of Ardabil in 2018 (using Air Q + model). Raw data related to particles were collected from the Department of Environment and processed in Excel software and converted into an input file of the Air Q + model, and in the final stage, by considering appropriate epidemiological parameters and combining these data with air quality data, it was possible to estimate the health effects of air pollution. The results showed that the average annual concentrations of PM_2.5 and PM₁₀ were 15.47 and 30.94 in the study year, respectively. The total number of deaths due to ALRI, COPD, lung cancer, IHD, and stroke deaths on average during the study period were estimated to be 73, 11, 7, 15, and 14 deaths, respectively, which include 14.62, 15.78, 4.9, 12.43, and 11.6% of deaths due to ALRI, COPD, lung cancer, IHD, and stroke deaths, respectively. In conditions of concentration above 5 µg/m³, attributed proportion, total number of attributed cases and number of attributed cases per 100,000 population (with moderate relative risk and confidence interval of 95%) for cardiovascular diseases have been estimated to be 0.95% 103 people and 42.19 people. Also, the attributed proportion, the total number of attributable cases, and the number of attributable cases per 100,000 population (with moderate relative risk and confidence of 95%) for the admission of respiratory diseases have been estimated at 97.1%, 68 persons and 3 persons, respectively. Our results suggest that particle exposure even at low concentrations is associated with an increased risk of overall mortality and specific cause mortality and hospital admissions for respiratory and cardiovascular diseases.

Spatio-Temporal Variation and Influencing Factors of Ozone Pollution in Beijing

The temporal and spatial distribution and variation characteristics of ozone (O3) in Beijing, China, are investigated using hourly monitoring data from 2020. Kriging interpolation analysis and correlation analysis are applied to describe the spatial-temporal distribution and to identify associated influencing factors. The average concentration of O3 was found to be 59.58 μg·m−3. The daily maximum sliding 8 h average ozone concentration values exceeded the primary standard (100 μg·m−3) for 129 days and exceeded the secondary standard (160 μg·m−3) for 48 days. Temporally, the general pattern of daily maximum 8 h average O3 concentration was high in spring and summer and low in autumn and winter. Monthly average values showed a maximum in June. The highest daily concentrations appeared between 13:00 and 18:00 local time, and O3 concentrations had a distinct weekly pattern of variability with daily average concentrations at weekends higher than those during working days. Spatially, annual average O3 concentrations were highest in the northeast and lowest in the southeast of the city, and the seasonal variation of O3 was most significant in the southwest of the city. In relation to city districts and counties, the annual average O3 concentrations in the Miyun District were the highest, while those in the Haidian District were the lowest. On the whole, annual average O3 concentrations in Beijing were higher in the suburbs than in central areas. Based on daily average values, there was no significant correlation between O3 concentrations and rainfall (p > 0.05), but there were significant correlations between O3 concentration and sunshine hours, wind speed, maximum temperature and minimum temperature (p < 0.05), with correlation coefficients of 0.158, 0.267, 0.724 and 0.703, respectively. O3 concentrations increased with an increasing number of sunshine hours, first increased and then decreased with increasing wind speed and increased with increasing temperature. O3 concentrations were correlated with SO2 concentrations (0.05 < p > 0.001), CO concentrations (p < 0.001) and NO2 concentrations (p < 0.001), the latter having the highest correlation coefficient of −0.553 and exhibiting opposite trends in daily and monthly variations to O3 variations. Analysis of ozone pollution sources showed that automobile exhaust, coal and oil combustion and volatile organic compounds released by industrial plants were the main sources. Terrain affected the distribution of ozone, as well as human activities and industry.

Exposure to long-range transported particulate matter and modeling age-related particle deposition

Exposure to particulate matter (PM) is known to cause cardiovascular disease and increase mortality and morbidity. Asian dust (AD) is a meteorological phenomenon which affects much of East Asia year-round but especially during the spring months. Here, we have characterized concentrations of PM₁₀ and classified synoptic air flow trajectories using HYSPLIT model for Asian dust events (from March to April) in Jeju island, Korea. The ADE is a phenomenon in which sand and dust in the deserts of China or Mongolia rise mainly in spring and are blown away by western winds and gradually subside. The calculated inhaled PM₁₀ doses from specific microenvironments (home, work or school, and transportation) were from 5.28 to 101.48 μg depending on age group and different microenvironments while the calculated PM₁₀ inhaled doses for ADE ranged within 67.92 -769.27 μg. Also, we have evaluated the contribution of specific microenvironments to the exposure for different age groups using time-activity patterns and calculated inhaled PM₁₀ doses and deposited mass/mass flux so as to estimate exposure using multiple-path particle dosimetry (MPPD) model. The monthly average outdoor PM₁₀ concentration range was 29.3-65.4 μg/m³, whereas the monthly PM₁₀ concentration for ADE was 127.0-342.0 μg/m³. Air masses from clusters 1 and 2 were 24% and 29% (in 2017), clusters 2 and 3 were 24% and 32% (in 2018), and clusters 1 and 3 were 28% and 26% (in 2019) for ADE. In the aerosol deposition based on MPPD model, the corresponding values for daily particle deposited mass for two age groups ranged from 8.64 ×10^-5 μg (age 8) to 8.64 ×10^-4 μg (age 21). We assessed the PM_2.5 exposure considering time-activity patterns, age groups, and ADE exposure evaluation caused by long-range transport airflow; this could be helpful for assessing PM₁₀ exposure-related health evaluation.

Estimating Intra-Urban Inequities in PM2.5-Attributable Health Impacts: A Case Study for Washington, DC

Air pollution levels are uneven within cities, contributing to persistent health disparities between neighborhoods and population sub-groups. Highly spatially resolved information on pollution levels and disease rates is necessary to characterize inequities in air pollution exposure and related health risks. We leverage recent advances in deriving surface pollution levels from satellite remote sensing and granular data in disease rates for one city, Washington, DC, to assess intra-urban heterogeneity in fine particulate matter (PM2.5)- attributable mortality and morbidity. We estimate PM2.5-attributable cases of all-cause mortality, chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, stroke, and asthma emergency department (ED) visits using epidemiologically derived health impact functions. Data inputs include satellite-derived annual mean surface PM2.5 concentrations; age-resolved population estimates; and statistical neighborhood-, zip code- and ward-scale disease counts. We find that PM2.5 concentrations and associated health burdens have decreased in DC between 2000 and 2018, from approximately 240 to 120 cause-specific deaths and from 40 to 30 asthma ED visits per year (between 2014 and 2018). However, remaining PM2.5-attributable health risks are unevenly and inequitably distributed across the District. Higher PM2.5-attributable disease burdens were found in neighborhoods with larger proportions of people of color, lower household income, and lower educational attainment. Our study adds to the growing body of literature documenting the inequity in air pollution exposure levels and pollution health risks between population sub-groups, and highlights the need for both high-resolution disease rates and concentration estimates for understanding intra-urban disparities in air pollution-related health risks.

Low-Cost Air Quality Stations’ Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment

Low-cost air quality stations can provide useful data that can offer a complete picture of urban air quality dynamics, especially when integrated with daily measurements from reference air quality stations. However, the success of such deployment depends on the measurement accuracy and the capability of resolving spatial and temporal gradients within a spatial domain. In this work, an ensemble of three low-cost stations named “AirQino” was deployed to monitor particulate matter (PM) concentrations over three different sites in an area affected by poor air quality conditions. Data of PM2.5 and PM10 concentrations were collected for about two years following a protocol based on field calibration and validation with a reference station. Results indicated that: (i) AirQino station measurements were accurate and stable during co-location periods over time (R2 = 0.5–0.83 and RMSE = 6.4–11.2 μg m−3; valid data: 87.7–95.7%), resolving current spatial and temporal gradients; (ii) spatial variability of anthropogenic emissions was mainly due to extensive use of wood for household heating; (iii) the high temporal resolution made it possible to detect time occurrence and strength of PM10 concentration peaks; (iv) the number of episodes above the 1-h threshold of 90 μg m−3 and their persistence were higher under urban and industrial sites compared to the rural area.

Defining pediatric asthma: phenotypes to endotypes and beyond

Asthma is the most common chronic pediatric lung disease that has traditionally been defined as a syndrome of airway inflammation characterized by clinical symptoms of cough and wheeze. Highlighting the complex and heterogeneous nature of asthma, this review summarizes recent advances in asthma classification that are based on pathobiology, and thereby directly addresses limitations of existent definitions of asthma. By reviewing and contrasting clinical and mechanistic features of adult and childhood asthma, the review summarizes key biomarkers that distinguish childhood asthma subtypes. While atopy and its severity are important features of childhood asthma, there is evidence to support the existence of a childhood asthma endotype distinct from the atopic endotype. Although biomarkers of non-atopic asthma are an area of future research, we summarize a clinical approach that includes existing measures of airway-specific and systemic measures of atopy, co-existing morbidities, and disease severity and control, in the definition of childhood asthma, to empower health care providers to better characterize asthma disease burden in children. Identification of biomarkers of non-atopic asthma and the contribution of genetics and epigenetics to pediatric asthma burden remains a research need, which can potentially allow delivery of precision medicine to pediatric asthma. IMPACT: This review highlights asthma as a complex and heterogeneous disease and discusses recent advances in the understanding of the pathobiology of asthma to demonstrate the need for a more nuanced definitions of asthma. We review current knowledge of asthma phenotypes and endotypes and put forth an approach to endotyping asthma that may be useful for defining asthma for clinical care as well as for future research studies in the realm of personalized medicine for asthma.

Regression-based flexible models for photochemical air pollutants in the national capital territory of megacity Delhi

Modelling photochemical pollutants, such as ground level ozone (O₃), nitric oxide (NO) and nitrogen dioxide (NO₂), in urban terrain was proven to be cardinal, chronophagous and complex. We built linear regression and random forest regression models using 4-years (2015-2018; hourly-averaged) observations for forecasting O₃, NO and NO₂ levels for two scenarios (1-month prediction (for January 2019) and 1-year prediction (for 2019)) - with and without the impact of meteorology. These flexible models have been developed for, both, localised (site-specific models) and combined (indicative of city-level) cases. Both models were aided with machine learning, to reduce their time-intensity compared to models built over high-performance computing. O₃ prediction performance of linear regression model at the city level, under both cases of meteorological consideration, was found to be significantly poor. However, the site-specific model with meteorology performed satisfactorily (r = 0.87; RK Puram site). Further, during testing, linear regression models (site-specific and combined) for NO and NO₂ with meteorology, show a slight improvement in their prediction accuracies when compared to the corresponding equivalent linear models without meteorology. Random forest regression with meteorology performed satisfactorily for indicative city-level NO (r = 0.90), NO₂ (r = 0.89) and O₃ (r = 0.85). In both regression techniques, increased uncertainty in modelling O₃ is attributed to it being a secondary pollutant, non-linear dependency on NO_x, VOCs, CO, radicals, and micro-climatic meteorological parameters. Analysis of importance among various precursors and meteorology have also been computed. The study holistically concludes that site-specific models with meteorology perform satisfactorily for both linear regression and random forest regression.

Influence of particulate matter on human health in selected African provinces: mini-review

Air contamination influenced the human health and environmental well-being of the ecosystem. Particulate matter is a series of issues from major air pollutants in atmosphere. The aim of the review was to analyses the influence of particulate matter on human health and estimate the number of populations exposed to air pollution. The data analysed using the Environmental Benefits Mapping Analysis program model to selected African provinces. The review used 15% rollback data from the global burden disease and 5.8 µg/m³ the concentration of air pollutants from 1990 to 2013 years. The main findings of the study revealed that about 370 million (36.6%) population affected by air pollution. Besides, the risk factor associated with a population was 53,000 deaths per total population and 50,000 life-year losses. The economic value estimated to avoid a single case of particular matter on human health effect were estimated 14 billion dollars (US 2011). Priorities should be given to air quality management to improve the human and environmental health of ecosystems to reduce the global burden of disease of Africa regions.

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

BACKGROUND

Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.

OBJECTIVES

We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.

METHODS

We estimated mortality and morbidity attributable to nitrogen dioxide (NO 2 ), black carbon (BC), and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO 2 and BC; and b) models predicting annual NO 2 , BC and PM 2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.

RESULTS

Estimated pollution-attributable deaths per 100,000 people at the 100 -m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO 2 [mean = 30 (95% CI: 9, 50)], BC [mean = 2 (95% CI: 1, 2)], and PM 2.5 , [mean = 49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO 2 -attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean = 64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean = 101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO 2 and PM 2.5 , with more spatial heterogeneity at the grid-cell-level [NO 2 CBG mean = 41 deaths per 100,000 people (95% CI: 12, 68); NO 2 county mean = 38 (95% CI: 11, 64); PM 2.5 CBG mean = 59 (95% CI: 40, 77); and PM 2.5 county mean = 55 (95% CI: 37, 71)].

DISCUSSION

Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679.

Air Pollution Health Risk Assessment (AP-HRA), Principles and Applications

Air pollution is a major public health problem. A significant number of epidemiological studies have found a correlation between air quality and a wide variety of adverse health impacts emphasizing a considerable role of air pollution in the disease burden in the general population ranging from subclinical effects to premature death. Health risk assessment of air quality can play a key role at individual and global health promotion and disease prevention levels. The Air Pollution Health Risk Assessment (AP-HRA) forecasts the expected health effect of policies impacting air quality under the various policy, environmental and socio-economic circumstances, making it a key tool for guiding public policy decisions. This paper presents the concept of AP-HRA and offers an outline for the proper conducting of AP-HRA for different scenarios, explaining in broad terms how the health hazards of air emissions and their origins are measured and how air pollution-related impacts are quantified. In this paper, seven widely used AP-HRA tools will be deeply explored, taking into account their spatial resolution, technological factors, pollutants addressed, geographical scale, quantified health effects, method of classification, and operational characteristics. Finally, a comparative analysis of the proposed tools will be conducted, using the SWOT (strengths, weaknesses, opportunities, and threats) method.

Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration

The distribution and dynamics of atmospheric pollutants are spatiotemporally heterogeneous due to variability in emissions, transport, chemistry, and deposition. To understand these processes at high spatiotemporal resolution and their implications for air quality and personal exposure, we present custom, low-cost air quality monitors that measure concentrations of contaminants relevant to human health and climate, including gases (e.g., O₃, NO, NO₂, CO, CO₂, CH₄, and SO₂) and size-resolved (0.3-10 μm) particulate matter. The devices transmit sensor data and location via cellular communications and are capable of providing concentration data down to second-level temporal resolution. We produce two models: one designed for stationary (or mobile platform) operation and a wearable, portable model for directly measuring personal exposure in the breathing zone. To address persistent problems with sensor drift and environmental sensitivities (e.g., relative humidity and temperature), we present the first online calibration system designed specifically for low-cost air quality sensors to calibrate zero and span concentrations at hourly to weekly intervals. Monitors are tested and validated in a number of environments across multiple outdoor and indoor sites in New Haven, CT; Baltimore, MD; and New York City. The evaluated pollutants (O₃, NO₂, NO, CO, CO₂, and PM_2.5) performed well against reference instrumentation (e.g., r = 0.66-0.98) in urban field evaluations with fast e-folding response times (≤1 min), making them suitable for both large-scale network deployments and smaller-scale targeted experiments at a wide range of temporal resolutions. We also provide a discussion of best practices on monitor design, construction, systematic testing, and deployment.

Potential health benefits of sustained air quality improvements in New York City: A simulation based on air pollution levels during the COVID-19 shutdown

New York City (NYC) experienced a sharp decline in air pollution during the COVID-19 shutdown period (March 15, 2020 to May 15, 2020)-albeit at high social and economic costs. It provided a unique opportunity to simulate a scenario in which the city-wide air quality improvement during the shutdown were sustained over the five-year period, 2021 through 2025, allowing us to estimate the potential public health benefits to children and adults and their associated economic benefits. We focused on fine particulate matter (PM_2.5) and modeled potential future health benefits to children and adults. The analysis considered outcomes in children that have not generally been accounted for in clean air benefits assessments, including preterm birth, term low birthweight, infant mortality, child asthma incidence, child asthma hospital admissions and emergency department visits, autism spectrum disorder, as well as adult mortality. We estimated a city-wide 23% improvement in PM_2.5 levels during the COVID-19 shutdown months compared to the average level for those months in 2015-2018 (the business as usual period). Based on the data for 2020, we extrapolated the ambient levels of PM_2.5 for the following five-year period. The estimated cumulative benefits for 2021-2025 included thousands of avoided cases of illness and death, with associated economic benefits from $31.8 billion to $77 billion. This "natural experiment," tragic though the cause, has provided a hypothetical clean air scenario that can be considered aspirational-one that could be achieved through transportation, climate, and environmental policies that support robust economic recovery with similarly reduced emissions.

Measuring The Impact Of Air Pollution On Health Care Costs

Air pollution contributes to the development of numerous adverse human health outcomes. The Environmental Protection Agency's Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) tool is widely used in estimating the health care costs of air pollution and in the development of federal and state regulations and policy. Its default features consider only the costs of hospital and emergency department admissions. A more complete accounting of the chain of costs would include ambulatory and other care. In this article we use employer health insurance claims data to infer additional costs that accompany hospitalizations but are not included in BenMAP-CE. Including additional categories increases BenMAP-CE health care cost estimates by approximately 40 percent for respiratory and cardiovascular patients. That is, for each dollar of health care costs captured by BenMAP-CE, a more complete accounting would include an additional 40 cents. These results suggest that because such air pollution costs are underestimated, the health care benefits associated with reducing air pollution may be much larger than previously estimated.

Quantifying the impact of particle matter on mortality and hospitalizations in four Brazilian metropolitan areas

Air quality management involves investigating areas where pollutant concentrations are above guideline or standard values to minimize its effect on human health. Particulate matter (PM) is one of the most studied pollutants, and its relationship with health has been widely outlined. To guide the construction and improvement of air quality policies, the impact of PM on the four Brazilian southeast metropolitan areas was investigated. One-year long modeling of PM₁₀ and PM_2.5 was performed with the WRF-Chem model for 2015 to quantify daily and annual PM concentrations in 102 cities. Avoidable mortality due to diverse causes and morbidity due to respiratory and circular system diseases were estimated concerning WHO guidelines, which was adopted in Brazil as a final standard to be reached in the future; although there is no deadline set for its implementation yet. Results showed satisfactory representation of meteorology and ambient PM concentrations. An overestimation in PM concentrations for some monitoring stations was observed, mainly in São Paulo metropolitan area. Cities around capitals with high modelled annual PM_2.5 concentrations do not monitor this pollutant. The total avoidable deaths estimated for the region, related to PM_2.5, were 32,000 ± 5,300 due to all-cause mortality, between 16,000 ± 2,100 and 51,000 ± 3,000 due non-accidental causes, between 7,300 ± 1,300 and 16,700 ± 1,500 due to cardiovascular disease, between 4,750 ± 900 and 10,950 ± 870 due ischemic heart diseases and 1,220 ± 330 avoidable deaths due to lung cancer. Avoidable respiratory hospitalizations were greater for PM_2.5 among 'children' age group than for PM₁₀ (all age group) except in São Paulo metropolitan area. For circulatory system diseases, 9,840 ± 3,950 avoidable hospitalizations in the elderly related to a decrease in PM_2.5 concentrations were estimated. This study endorses that more restrictive air quality standards, human exposure, and health effects are essential factors to consider in urban air quality management.

Assessing Air Quality and Public Health Benefits of New York City's Climate Action Plans

Strategies that reduce greenhouse gas (GHG) emissions may also provide significant public health benefits and their estimation can help prioritize the case for climate change mitigation policies. In 2014, New York City (NYC) committed to reduce GHG emissions by 80% by 2050 (80 × 50). In this analysis we quantified the air quality-related public health benefits of the policies outlined in the 80 × 50 strategy, compared sector-specific (buildings, energy, transportation) policy impacts, and assessed variations in benefits across NYC neighborhoods. We applied air quality modeling and health impact assessment tools to estimate expected changes in ambient PM_2.5 and related health outcomes by Zip Code Tabulation Areas (ZCTA). Full implementation of 80 × 50 strategies would reduce PM_2.5 (fine particulates) concentrations across NYC ZCTAs by 7% (3%, 10%) (ZCTA median, 10th, 90th percentile), avoiding between 160 and 390 premature deaths and 460 hospitalizations and emergency department visits for respiratory and cardiovascular disease each year, valued at $3.4 billion annually. Across all the policy scenarios we estimated 10 times more avoided asthma emergency department visits in low-income neighborhoods as compared to the wealthiest neighborhoods even though median declines in ambient PM_2.5 were similar. Consideration of public health benefits helps to prioritize climate policy implementation and identify priority neighborhoods.

Association between microenvironment air quality and cardiovascular health outcomes

Exposure to fine particulate matter (PM_2.5) is associated with cardiovascular disease risk. To date, there are few studies on short-term PM_2.5 exposure in different microenvironments and its impact on immediate health effects, particularly in the Southeast Asia region. This study assessed PM_2.5 concentrations in different microenvironments in a densely populated city in the tropics using low-cost personal PM_2.5 sensors as well as their associations with acute cardiovascular health outcomes. A total of 49 adult participants affiliated with the National University of Singapore (NUS) community were recruited. Personal low-cost sensors were used to measure PM_2.5 concentrations between September 2017 and March 2019. Demographic information and time-activity patterns were collected using questionnaires. Wilcoxon pairwise comparisons were used to determine statistical differences between PM_2.5 exposures at 18 different microenvironments. Generalized Estimating Equations (GEE) models were used to assess the association between PM_2.5 exposure and blood pressure as well as heart rate. All models were adjusted for age, sex, body mass index, physical activity, temperature, duration of exposure, and baseline cardiovascular parameters. Significant differences in PM_2.5 concentrations were observed across different microenvironments. Air-conditioned offices and tertiary teaching spaces had the lowest (median = 13.1 μg/m³) and hawker centres had the highest (median = 32.0 μg/m³) PM_2.5 concentrations. Significant positive associations between PM_2.5 exposure and heart rate (β = 0.40, p = 4.6 × 10^-5) as well as diastolic blood pressure (β = 0.16, p = 0.0077) were also observed. Short-term exposure to PM_2.5 was significantly associated with higher heart rate and blood pressure. Further work is needed to investigate the variations within each type of microenvironment and expand the study to other sub-populations such as the elderly and children.

Association Between Outdoor Air Pollution and Risk of Malignant and Benign Brain Tumors: The Multiethnic Cohort Study

BACKGROUND

There are increasing concerns about the potential impact of air pollution on chronic brain inflammation and microglia cell activation, but evidence of its carcinogenic effects is limited.

METHODS

We used kriging interpolation and land use regression models to estimate long-term air pollutant exposures of oxides of nitrogen (NOx, NO2), kriging interpolation for ozone (O3), carbon monoxide, and particulate matter (PM2.5, PM10), and nearest monitoring station measurements for benzene for 103 308 men and women from the Multiethnic Cohort, residing largely in Los Angeles County from recruitment (1993-1996) through 2013. We used Cox proportional hazards models to examine the associations between time-varying pollutants and risk of malignant brain cancer (94 men, 116 women) and meningioma (130 men, 425 women) with adjustment for sex, race and ethnicity, neighborhood socioeconomic status, smoking, occupation, and other covariates. Stratified analyses were conducted by sex and race and ethnicity.

RESULTS

Brain cancer risk in men increased in association with exposure to benzene (hazard ratio [HR] = 3.52, 95% confidence interval [CI] = 1.55 to 7.55) and PM10 (HR = 1.80, 95% CI = 1.00 to 3.23). Stronger associations with PM10 (HR = 3.02, 95% CI = 1.26 to 7.23), O3 (HR = 2.93, 95% CI = 1.09 to 7.88), and benzene (HR = 4.06, 95% CI = 1.17 to 18.2) were observed among Latino men. Air pollution was unrelated to risk of meningioma except that O3 exposure was associated with risk in men (HR = 1.77, 95% CI = 1.02 to 3.06). Brain cancer risk in women was unrelated to air pollution exposures.

CONCLUSIONS

Confirmation of these sex differences in air pollution-brain cancer associations and the stronger findings in Latino men in additional diverse populations is warranted.

Estimating the daily PM2.5 concentration in the Beijing-Tianjin-Hebei region using a random forest model with a 0.01° × 0.01° spatial resolution

High spatiotemporal resolution fine particulate matter (PM_2.5) simulations can provide important exposure data for the assessment of long-term and short-term health effects. Satellite-based aerosol optical depth (AOD) data, meteorological data, and topographic data have become key variables for PM_2.5 estimation. In this study, a random forest model was developed and used to estimate the highest resolution (0.01° × 0.01°) daily PM_2.5 concentrations in the Beijing-Tianjin-Hebei region. Our model had a suitable performance (cv-R² = 0.83 and test-R² = 0.86). The regional test-R² value in southern Beijing-Tianjin-Hebei was higher than that in northern Beijing-Tianjin-Hebei. The model performance was excellent at medium to high PM_2.5 concentrations. Our study considered meteorological lag effects and found that the boundary layer height of the one-day lag had the most important contribution to the model. AOD and elevation factors were also important factors in the modeling process. High spatiotemporal resolution PM_2.5 concentrations in 2010-2016 were estimated using a random forest model, which was based on PM_2.5 measurements from 2013 to 2016.

Estimating daily PM2.5 concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements

Previous PM_2.5 related epidemiological studies mainly relied on data from sparse regulatory monitors to assess exposure. The introduction of non-regulatory PM_2.5 monitors presents both opportunities and challenges to researchers and air quality managers. In this study, we evaluated the advantages and limitations of integrating non-regulatory PM_2.5 measurements into a satellite-based daily PM_2.5 model at 100 m resolution in New York City in 2015. Two separate machine learning models were developed, one using only PM_2.5 data from the US Environmental Protection Agency (EPA), and the other with measurements from both EPA and the New York City Community Air Survey (NYCCAS). The EPA-only model obtained a cross-validation (CV) R² of 0.85 while the EPA + NYCCAS model obtained a CV R² of 0.73. With the help of the NYCCAS measurements, the EPA + NYCCAS model predicted distinctly different PM_2.5 spatial patterns and more pollution hotspots compared with the EPA model, and its predictions were >15% higher than the EPA model along major roads and in densely populated areas. Our results indicated that satellite AOD and non-regulatory PM_2.5 measurements can be fused together to capture neighborhood-scale PM_2.5 levels and previous studies may have underestimated the disease burden due to PM_2.5 in densely populated areas.

Health impacts and cost-benefit analyses of surface O3 and PM2.5 over the U.S. under future climate and emission scenarios

Health impacts of surface ozone (O₃) and fine particulate matter (PM_2.5) are of major concern worldwide. In this work, the Environmental Benefits Mapping and Analysis Program tool is applied to estimate the health and economic impacts of projected changes in O₃ and PM_2.5 in the U.S. in future (2046-2055) decade relative to current (2001-2010) decade under the Representative Concentration Pathway (RCP) 4.5 and 8.5 climate scenarios. Future annual-mean O₃ reductions under RCP 4.5 prevent ~1,800 all-cause mortality, 761 respiratory hospital admissions (HA), and ~1.2 million school loss days annually, and result in economic benefits of ~16 billion, 29 million, and 132 million U.S. dollars (USD), respectively. By contrast, the projected future annual-mean O₃ increases under RCP8.5 cause ~2,400 mortality, 941 respiratory HA, and ~1.6 million school loss days annually and result in economic disbenefits of ~21 billion, 36 million, and 175 million USD, respectively. Health benefits of reduced O₃ double under RCP4.5 and health dis-benefits of increased O₃ increase by 1.5 times under RCP8.5 in future with 2050 population and baseline incidence rate. Because of the reduction in projected future PM_2.5 over CONUS under both scenarios, the annual avoided all-cause deaths, cardiovascular HA, respiratory HA, and work loss days are ~63,000 and ~83,000, ~5,300 and ~7,000, ~12,000 and ~15,000, and ~7.8 million and ~10 million, respectively, leading to economic benefits of ~560 and ~740 billion, ~240 and ~320 million, ~450 and ~590 million, and ~1,400 and ~1,900 million USD for RCP4.5 and 8.5, respectively. Health benefits of reduced PM_2.5 for future almost double under both scenarios with the largest benefits in urban areas. RCP8.5 projects larger health and economic benefits due to a greater reduction in PM_2.5 but with a warmer atmosphere and higher O₃ pollution than RCP4.5. RCP4.5 leads to multiple-benefit goals including reduced O₃ and PM_2.5, reduced mortality and morbidity, and saved costs. Greater reduction in future PM_2.5 under RCP4.5 should be considered to achieve larger multi-benefits.

The air quality and health impacts of projected long-haul truck and rail freight transportation in the United States in 2050

Diesel emissions from freight transportation activities are a key threat to public health. This study examined the air quality and public health impacts of projected freight-related emissions in 2050 over the continental United States. Three emission scenarios were considered: (1) a projected business-as-usual socioeconomic growth with freight fleet turnover and stringent emission control (CTR); (2) the application of a carbon pricing climate policy (PO); and (3) further technology improvements to eliminate high-emitting conditions in the truck fleet (NS). The PO and NS cases are superimposed on the CTR case. Using a WRF-SMOKE-CMAQ-BenMAP modeling framework, we quantified the impacts of diesel fine particulate matter (PM_2.5) emissions change on air quality, health, and economic benefits. In the CTR case, we simulate a widespread reduction of PM_2.5 concentrations, between 0.5 and 1.5 μg m^-3, comparing to a base year of 2011. This translates into health benefits of 3600 (95% CI: 2400-4800) prevented premature deaths, corresponding to $38 (95% CI: $3.5-$100) billion. Compared to CTR case, the PO case can obtain ~9% more health benefits nationally, however, climate policy also affects the health outcomes regionally due to transition of demand from truck to rail; regions with fewer trucks could gain in health benefits, while regions with added rail freight may potentially experience a loss in health benefits due to air quality degradation. The NS case provides substantial additional benefits (~20%). These results support that a combination of continuous adoption of stringent emission standards and strong improvements in vehicle technology are necessary, as well as rewarding, to meet the sustainable freight and community health goals. States and metropolitan areas with high population density and usually high freight demand and emissions can take more immediate actions, such as accelerating vehicle technology improvements and removing high-emitting trucks, to improve air quality and health benefits.

Longitudinal Effect of Ambient Air Pollution and Pollen Exposure on Asthma Control: The Patient-Reported Outcomes Measurement Information System (PROMIS) Pediatric Asthma Study

OBJECTIVE

Although exposure to air pollution and pollen is associated with asthma exacerbation and increased health care use, longitudinal effects of fine particulate matter 2.5 (PM_2.5), ozone (O₃), and pollen exposure on asthma control status in pediatric patients are understudied. This study investigated effects of exposure to PM_2.5, O₃, and pollen on asthma control status among pediatric patients with asthma.

METHODS

A total of 229 dyads of pediatric patients with asthma and their parents were followed for 15 months. The Asthma Control and Communication Instrument was used to measure asthma control, which was reported weekly by parents during a 26-week period. PM_2.5 and O₃ data were collected from the US Environmental Protection Agency Air Quality System. Pollen data were obtained from Intercontinental Marketing Services Health. Mean air pollutant and pollen exposures within 7 days before the reporting of asthma control were used to estimate weekly exposures for each participant. Linear mixed-effects models were performed to test associations of PM_2.5, O₃, and pollen exposure with asthma control status. Sensitivity analyses were performed to evaluate the robustness of findings by different exposure monitoring days per week and distances between monitoring sites and participants' residences.

RESULTS

Elevated PM_2.5 concentration and pollen severity were associated with poorer asthma control status (P < .05), yet elevated O₃ concentration was marginally associated with better asthma control (P < .1).

CONCLUSIONS

Poorer asthma control status was associated with elevated PM_2.5 and pollen severity. Reducing harmful outdoor environmental ambient exposure may improve asthma outcomes in children and adolescents.

Violent crime and socioeconomic deprivation in shaping asthma-related pollution susceptibility: a case-crossover design

Background

The objective of this study was to quantify and compare the relative influence of community violent crime and socioeconomic deprivation in modifying associations between ozone and emergency department (ED) visits for asthma among children.

Methods

We used a spatiotemporal case-crossover analysis for all New York City EDs for the months May–September from 2005 to 2011 from a statewide administrative ED dataset. The data included 11 719 asthmatic children aged 5–18 years, and the main outcome measure was percentage of excess risk of asthma ED visit based on Cox regression analysis.

Results

Stronger ozone–asthma associations were observed for both elevated crime and deprivation (eg, on lag day 2, we found 20.0% (95% CI 10.2% to 30.6 %) and 21.0% (10.5% to 32.5%) increased risk per 10 ppb ozone, for communities in the highest vs lowest quartiles of violent crime and deprivation, respectively). However, in varied models accounting for both modifiers, only violence retained significance.

Conclusions

The results suggest stronger spatiotemporal ozone–asthma associations in communities of higher violent crime or deprivation. Notably, violence was the more consistent and significant modifier, potentially mediating a substantial portion of socioeconomic position–related susceptibility.

Revisiting the estimations of PM2.5-attributable mortality with advancements in PM2.5 mapping and mortality statistics

With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM_2.5) and mortality statistics are increasingly used to examine the health effects of PM_2.5. Choices of these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM_2.5-attributable mortality by taking advantage of recent advancements in high resolution mapping of PM_2.5concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM_2.5-mortality for the years of 2000, 2005, 2010 and 2015 using three PM_2.5 concentration datasets [Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR)] at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM_2.5-mortality from the 10 km CTM-derived PM_2.5 dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM_2.5 datasets. The estimated PM_2.5-mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM_2.5 concentrations reduction, the uses of the two newly developed 1 km resolution PM_2.5 datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM_2.5 concentration is the primary factor that leads to the PM_2.5-attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM_2.5-attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. CAPSULE: We revisited the estimations of PM_2.5-attributable mortality with advancements in PM_2.5 mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations.

Spatial-temporal variations of summertime ozone concentrations across a metropolitan area using a network of low-cost monitors to develop 24 hourly land-use regression models

Ten relatively-low-cost ozone monitors (Aeroqual Series 500 with OZL ozone sensor) were deployed to assess the spatial and temporal variability of ambient ozone concentrations across residential areas in the Monroe County, New York from June to October 2017. The monitors were calibrated in the laboratory and then deployed to a local air quality monitoring site where they were compared to the federal equivalent method values. These correlations were used to correct the measured ozone concentrations. The values were also used to develop hourly land use regression models (LUR) based on the deletion/substitution/addition (D/S/A) algorithm that can be used to predict the spatial and temporal concentrations of ozone at any hour of a summertime day and given location in Monroe County. Adjusted R2 values were high (average 0.83) with the highest adjusted R2 for the model between 8 and 9 AM (i.e. 1-2 h after the peak of primary emissions during the morning rush hours). Spatial predictors with the highest positive effects on ozone estimates were high intensity developed areas, low and medium intensity developed areas, forests + shrubs, average elevation, Interstate + highways, and the annual average vehicular daily traffic counts. These predictors are associated with potential emissions of anthropogenic and biogenic precursors. Maps developed from the models exhibited reasonable spatial and temporal patterns, with low ozone concentrations overnight and the highest concentrations between 11 AM and 5 PM. The adjusted R2 between the model predictions and the measured values varied between 0.79 and 0.87 (mean = 0.83). The combined use of the network of low-cost monitors and LUR modeling provide useful estimates of intraurban ozone variability and exposure estimates that will be used in future epidemiological studies.

Air pollution, urgent asthma medical visits and the modifying effect of neighborhood asthma prevalence

BACKGROUND

Social and environmental stressors may modify associations between environmental pollutants and asthma symptoms. We examined if neighborhood asthma prevalence (higher: HAPN vs. lower: LAPN), a surrogate for underlying risk factors for asthma, modified the relationship between pollutants and urgent asthma visits.

METHODS

Through zip code, home addresses were linked to New York City Community Air Survey's land use regression model for street-level, annual average nitrogen dioxide (NO2), particulate matter (PM2.5), elemental carbon (EC), summer average ozone (O3), winter average sulfur dioxide (SO2) concentrations. Poisson regression models were fit to estimate the association (prevalence ratio, PR) between pollutant exposures and seeking urgent asthma care.

RESULTS

All pollutants, except O3 were higher in HAPN than LAPN (P < 0.01). Neighborhood asthma prevalence modified the relationship between pollutants and urgent asthma (P-interaction < 0.01, for NO2 and SO3). Associations between pollutants and urgent asthma were observed only in LAPN (NO2: PR = 1.38, P = 0.01; SO3: PR = 1.85, P = 0.04). No association was observed between pollutants and urgent asthma among children in HAPN (P > 0.05).

CONCLUSIONS

Relationships between modeled street-level pollutants and urgent asthma were stronger in LAPN compared to HAPN. Social stressors that may be more prevalent in HAPN than LAPN, could play a greater role in asthma exacerbations in HAPN vs. pollutant exposure alone.

The Use of Geographic Data to Improve Asthma Care Delivery and Population Health

The authors examine uses of geographic data to improve asthma care delivery and population health and describe potential practice changes and areas for future research.

Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors

A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO₂, NO₂, O₃, VOC, PM2.5 and PM10 were integrated on an Arduino Shield compatible board. As concerns PM2.5 and PM10 sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full "heating season" (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM2.5 and PM10 mean biases of 0.036 and 0.598 µg/m³, RMSE of 4.056 and 6.084 µg/m³, and R² of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.

Application of an Original Wildfire Smoke Health Cost Benefits Transfer Protocol to the Western US, 2005-2015

Recent growth in the frequency and severity of US wildfires has led to more wildfire smoke and increased public exposure to harmful air pollutants. Populations exposed to wildfire smoke experience a variety of negative health impacts, imposing economic costs on society. However, few estimates of smoke health costs exist and none for the entire Western US, in particular, which experiences some of the largest and most intense wildfires in the US. The lack of cost estimates is troublesome because smoke health impacts are an important consideration of the overall costs of wildfire. To address this gap, this study provides the first time series estimates of PM2.5 smoke costs across mortality and several morbidity measures for the Western US over 2005-2015. This time period includes smoke from several megafires and includes years of record-breaking acres burned. Smoke costs are estimated using a benefits transfer protocol developed for contexts when original health data are not available. The novelty of our protocol is that it synthesizes the literature on choices faced by researchers when conducting a smoke cost benefit transfer. On average, wildfire smoke in the Western US creates $165 million in annual morbidity and mortality health costs.

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

New Insights into Activity Patterns in Children, Found Using Functional Data Analyses

INTRODUCTION/PURPOSE

Continuous monitoring of activity using accelerometers and other wearable devices provides objective, unbiased measurement of physical activity in minute-by-minute or finer resolutions. Accelerometers have already been widely deployed in studies of healthy aging, recovery of function after heart surgery, and other outcomes. Although common analyses of accelerometer data focus on single summary variables, such as the total or average activity count, there is growing interest in the determinants of diurnal profiles of activity.

METHODS

We use tools from functional data analysis (FDA), an area with an established statistical literature, to treat complete 24-h diurnal profiles as outcomes in a regression model. We illustrate the use of such models by analyzing data collected in New York City from 420 children participating in a Head Start program. Covariates of interest include season, sex, body mass index z-score, presence of an asthma diagnosis, and mother's birthplace.

RESULTS

The FDA model finds several meaningful associations between several covariates and diurnal profiles of activity. In some cases, including shifted activity patterns for children of foreign-born mothers and time-specific effects of asthma on activity, these associations exist for covariates that are not associated with average activity count.

CONCLUSION

FDA provides a useful statistical framework for settings in which the effect of covariates on the timing of activity is of interest. The use of similar models in other applications should be considered, and we make code public to facilitate this process.

Estimating the Public Health Impact of Air Pollution for Informing Policy in the Twin Cities: A Minnesota Tracking Collaboration

OBJECTIVE

The Minnesota Department of Health and the Minnesota Pollution Control Agency used local air pollution and public health data to estimate the impacts of particulate matter and ozone on population health, to identify disparities, and to inform decisions that will improve health.

SETTING

While air quality in Minnesota currently meets federal standards, urban communities are concerned about the impact of air pollution on their health. The Twin Cities (Minneapolis-St Paul) metropolitan area includes 7 counties where fine particulate levels and rates of asthma exacerbations are elevated in some communities.

DESIGN

We used the Environmental Protection Agency's BenMAP (Environmental Benefits Mapping and Analysis Program) software, along with local PM2.5 (fine particulate) and ozone ambient concentrations, census and population health data, to calculate impacts for 2008 at the zip code level. The impacts were summed across all zip codes for area-wide estimates. American Community Survey data were used to stratify zip codes by poverty and race for assessment of disparities.

MAIN OUTCOME MEASURES

Attributable fraction, attributable rate and counts for all-cause mortality, asthma and chronic obstructive pulmonary disease hospitalizations, asthma emergency department (ED) visits, and cardiovascular disease hospitalizations.

RESULTS

In the Twin Cities (2008), air pollution was a contributing cause for an estimated 2% to 5% of respiratory and cardiovascular hospitalizations and ED visits and between 6% and 13% of premature deaths. The elderly (aged 65+ years) experienced the highest air pollution-attributable rates of death and respiratory hospitalizations; children experienced the highest asthma ED visit rates. Geographical and demographic differences in air pollution-attributable health impacts across the region reflected the differences in the underlying morbidity and mortality rates.

CONCLUSIONS

Method was effective in demonstrating that changes in air quality can have quantifiable health impacts across the Twin Cities. Key messages and implications from this work were shared with the media, community groups, legislators and the public. The results are being used to inform initiatives aimed at reducing sources of air pollution and to address health disparities in urban communities.

A Monte Carlo method for summing modeled and background pollutant concentrations

Air quality analyses for permitting new pollution sources often involve modeling dispersion of pollutants using models such as AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model). Representative background pollutant concentrations must be added to modeled concentrations to determine compliance with air quality standards. Summing 98th (or 99th) percentiles of two independent distributions that are unpaired in time overestimates air quality impacts and could needlessly burden sources with restrictive permit conditions. This problem is exacerbated when emissions and background concentrations peak during different seasons. Existing methods addressing this matter either require much input data, or disregard source and background seasonality, or disregard the variability of the background by utilizing a single concentration for each season, month, hour-of-day, day-of-week, or wind direction. Availability of representative background concentrations are another limitation. Here the authors report on work to improve permitting analyses, with the development of (1) daily gridded, background concentrations interpolated from 12-km CMAQ (Community Multiscale Air Quality Model) forecasts and monitored data. A two-step interpolation reproduced measured background concentrations to within 6.2%; and (2) a Monte Carlo (MC) method to combine AERMOD output and background concentrations while respecting their seasonality. The MC method randomly combines, with replacement, data from the same months and calculates 1000 estimates of the 98th or 99th percentiles. The design concentration of background + new source is the median of these 1000 estimates. It was found that the AERMOD design value (DV) + background DV lay at the upper end of the distribution of these one thousand 99th percentiles, whereas measured DVs were at the lower end. This MC method sits between these two metrics and is sufficiently protective of public health in that it overestimates design concentrations somewhat. The authors also calculated probabilities of exceeding specified thresholds at each receptor, better informing decision makers of new source air quality impacts. The MC method is executed with an R script, which is available freely upon request.

IMPLICATIONS

Summing representative background pollutant concentrations with air dispersion model output using a Monte Carlo method that respects the seasonality of each provides for more robust and scientifically defensible air quality analyses in support of permit applications. This work provides applicants a method to demonstrate compliance with National Ambient Air Quality Standards and avoid emission controls that might be based on overly conservative analyses. It also calculates the probability of exceeding the standard, allowing regulators to make more informed permitting decisions.

External costs of PM2.5 pollution in Beijing, China: Uncertainty analysis of multiple health impacts and costs

Some cities in China are facing serious air pollution problems including high concentrations of particles, SO₂ and NO_x. Exposure to PM2.5, one of the primary air pollutants in many cities in China, is highly correlated with various adverse health impacts and ultimately represents a cost for society. The aim of this study is to assess health impacts and external costs related to PM2.5 pollution in Beijing, China with different baseline concentrations and valuation methods. The idea is to provide a reasonable estimate of the total health impacts and external cost due to PM2.5 pollution, as well as a quantification of the relevant uncertainty. PM2.5 concentrations were retrieved for the entire 2012 period in 16 districts of Beijing. The various PM2.5 related health impacts were identified and classified to avoid double counting. Exposure-response coefficients were then obtained from literature. Both the value of statistical life (VSL) and the amended human capital (AHC) approach were applied for external costs estimation, which could provide the upper and lower bound of the external costs due to PM2.5. To fully understand the uncertainty levels, the external cost distribution was determined via Monte Carlo simulation based on the uncertainty of the parameters such as PM2.5 concentration, exposure-response coefficients, and economic cost per case. The results showed that the external costs were equivalent to around 0.3% (AHC, China's guideline: C₀ = 35 μg/m³) to 0.9% (VSL, WHO guideline: C₀ = 10 μg/m³) of regional GDP depending on the valuation method and on the assumed baseline PM2.5 concentration (C₀). Among all the health impacts, the economic loss due to premature deaths accounted for more than 80% of the overall external costs. The results of this study could help policymakers prioritizing the PM2.5 pollution control interventions and internalize the external costs through the application of economic policy instruments.

Estimation of the PM2.5 health effects in China during 2000-2011

Exposure to fine particulate matter (PM_2.5) has been associated with mortality, but the extent of the adverse impacts differs across various regions. A quantitative estimation of health effects attributed to PM_2.5 in China is urgently required, particularly because it has the largest population and high air pollution levels. Based on the remote sensing-derived PM_2.5 and grid population data, we estimated the acute health effects of PM_2.5 in China using an exposure-response function. The results suggest the following: (1) The proportion of the population exposed to high PM_2.5 concentrations (>35 μg/m³) increased consistently from 2000 to 2011, and the population exposed to concentrations above the threshold defined by World Health Organization (WHO) (>10 μg/m³) rose from 1,191,191,943 to 1,290,562,965. (2) The number of deaths associated with PM_2.5 exposure increased steadily from 107,608 in 2000 to 173,560 in 2010, with larger numbers in the eastern region. (3) PM_2.5 health effects decreased in three pollution control scenarios estimated for 2017, i.e., the Air Pollution Prevention and Control Action Plan (APPCAP) scenario, the APPCAP under WHO IT-1 scenario (35 μg/m³), and the APPCAP under WHO IT-3 scenario (15 μg/m³), which indicates that pollution control can effectively reduce PM_2.5 effects on mortality.

Seasonal variation of particle-induced oxidative potential of airborne particulate matter in Beijing

An in vitro plasmid scission assay (PSA), the cell apoptosis assay, and ICP-MS were employed to study the oxidative potentials and trace element compositions of the airborne particulate matter (PM) in Beijing during a one year-long field campaign from June 2010 to June 2011. The cell damages induced by PM reveled by the cell apoptosis assay showed a similar variation pattern to the DNA damages obtained by PSA, verifying the feasibility of the PSA in analyzing the oxidative capacity of PM samples. The PSA experiments showed that the particle-induced DNA damage was highest in summer, followed by spring, winter and autumn in descending order. The percentages of the oxidative damages to plasmid DNA induced by the water-soluble fractions of PM under the particle doses from 10 to 250μg/ml were generally lower than 45%, with some values peaking at above 50%. The peak values were frequently present in late spring (i.e. April and May) and early summer (i.e. June) but they were scarcely observed in other seasons. These peak values were mostly associated with haze days or the days with low wind speed (less than 4m/s), indicating that the PM samples during haze had higher oxidative potential than those during non-haze periods. The oxidative potential induced by the water-soluble fraction of the PM displayed a significant positive correlation with the concentrations of the water-soluble elements Cd, Cs, Pb, Rb, Zn, Be and Bi, demonstrating that the particle-induced oxidative potentials were mainly sourced from these elements. The exposure risk represented by the mass concentration of these elements in unit volume of atmosphere was higher in summer and winter, and lower in autumn and spring. The haze day PM samples not only had higher level of oxidative potentials but also had higher concentrations of water-soluble elements.

Population Epigenetics

The field of epigenetics is maturing, with increased interest in understanding the normal regulation of the genome and the possibility that it becomes reprogrammed aberrantly as part of the cause of disease phenotypes. Applying the current technologies and insights to the study of human populations is potentially a way of understanding mechanisms and consequences of these diseases. When extended to encompass health care disparities, understanding why certain populations are unusually prone to specific conditions, there is certainly some potential for gaining new and valuable insights, but these studies are likely to be unusually prone to the effects of confounding influences and need to be designed, executed and interpreted with extra care.