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The U.S. EPA MOVES3 model was used to assess the impact of the large-scale introduction of electric vehicles on emissions of criteria pollutants (CO, hydrocarbons [HC], NOx, and particulate matter [PM]) and CO2 from the U.S. light-duty vehicle fleet. Large reductions in emissions of these criteria pollutants occurred in 2000-2020. These trends are expected to continue through 2040 driven by turnover of the conventional fleet with old vehicles being replaced by battery electric vehicles (BEVs) and by new internal combustion engine vehicles (ICEVs) with modern emission control systems. Without the introduction of BEVs, the absolute emissions of CO, NOx, HC, and PM2.5 from the U.S. light-duty vehicle fleet are expected to decrease by approximately 61, 88, 55, and 20% from 2020 to 2040. Introduction of BEVs with market share increasing linearly to 100% in 2040 provides additional benefits, which, combined with ICEV fleet turnover, would lead to decreases of absolute emissions of CO, NOx, HC, and PM2.5 of approximately 77, 94, 71, and 37% from 2020 to 2040. Reductions in CO2 emissions follow a similar pattern. Large decreases in criteria pollutant and CO2 emissions from light duty vehicles lie ahead.

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The present work describes the training and subsequent implementation on an FPGA board of an LSTM neural network for the modeling and prediction of the exceedances of criteria pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM10 and PM2.5). Understanding the behavior of pollutants and assessing air quality in specific geographical regions is crucial. Overexposure to these pollutants can cause harm to both natural ecosystems and living organisms, including humans. Therefore, it is essential to develop a solution that can accurately evaluate pollution levels. One potential approach is to implement a modified LSTM neural network on an FPGA board. This implementation obtained an 11% improvement compared to the original LSTM network, demonstrating that the proposed architecture is able to maintain its functionality despite reducing the number of neurons in its initial layers. It shows the feasibility of integrating a prediction network into a limited system such as an FPGA board, but easily coupled to a different system. Importantly, this implementation does not compromise the prediction accuracy for both 24 h and 72 h time frames, highlighting an opportunity for further enhancement and refinement.

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Transportation network companies (TNCs), such as Uber and Lyft, have pledged to fully electrify their ridesourcing vehicle fleets by 2030 in the United States. In this paper, we introduce AgentX, a novel agent-based model built in Julia for simulating ridesourcing services with high geospatial and temporal resolution. We then instantiate this model to estimate the life cycle air pollution, greenhouse gas, and traffic externality benefits and costs of serving rides based on Chicago TNC trip data from 2019 to 2022 with fully electric vehicles. We estimate that electrification reduces life cycle greenhouse gas emissions by 40-45% (9-10¢ per trip) but increases life cycle externalities from criteria air pollutants by 6-11% (1-2¢ per trip) on average across our simulations, which represent demand patterns on weekdays and weekends across seasons during prepandemic, pandemic, and post-vaccination periods. A novel finding of our work, enabled by our high resolution simulation, is that electrification may increase deadheading for TNCs due to additional travel to and from charging stations. This extra vehicle travel increases estimated congestion, crash risk, and noise externalities by 2-3% (2-3¢ per trip). Overall, electrification reduces net external costs to society by 3-11% (5-24¢ per trip), depending on the assumed social cost of carbon.

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BACKGROUND: Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution. METHODS: We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant's address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures. RESULTS: Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E-2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E-3). SIGNIFICANCE: While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases. SIGNIFICANCE AND IMPACT STATEMENT: The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease.

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BACKGROUND: Motor vehicles, including public transit buses, are a major source of air pollution in New York City (NYC) and worldwide. To address this problem, governments and transit agencies have implemented policies to introduce cleaner vehicles into transit fleets. Beginning in 2000, the Metropolitan Transit Agency began deploying compressed natural gas, hybrid electric, and low-sulfur diesel buses to reduce urban air pollution. OBJECTIVE: We hypothesized that bus fleet changes incorporating cleaner vehicles would have detectable effects on air pollution concentrations between 2009 and 2014, as measured by the New York City Community Air Survey (NYCCAS). METHODS: Depot- and route-specific information allowed identification of areas with larger or smaller changes in the proportion of distance traveled by clean buses. Data were assembled for 9670 300 m × 300 m grid cell areas with annual concentration estimates for nitrogen oxide (NO), nitrogen dioxide (NO2), and black carbon (BC) from NYCCAS. Spatial error models adjusted for truck route presence and total traffic volume. RESULTS: While concentrations of all three pollutants declined between 2009 and 2014 even in the 39.7% of cells without bus service, the decline in concentrations of NO and NO2 was greater in areas with more bus service and with higher proportional shifts toward clean buses. Conversely, the decline in BC concentration was slower in areas with more bus service and higher proportional clean bus shifts. SIGNIFICANCE: These results provide evidence that the NYC clean bus program impacted concentrations of air pollution, particularly in reductions of NO2. Further work can investigate the potential impact of these changes on health outcomes in NYC residents. IMPACT STATEMENT: Urban air pollution from diesel-burning buses is an important health exposure. The New York Metropolitan Transit Agency has worked to deploy cleaner buses into their fleet, but the impact of this policy has not been evaluated. Successful reductions in air pollution are critical for public health.

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BACKGROUND: Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error. OBJECTIVE: To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits. METHODS: We obtained daily counts of ED visits for Atlanta, GA during 2009-2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics. RESULTS: Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging). SIGNIFICANCE: The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies. IMPACT STATEMENT: This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.

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The coronavirus (COVID-19) pandemic first impacted Thailand in early 2020. The government imposed lockdown measures from April to May 2020 to control the spread of infection. Daily lifestyles then morphed into a so-called new normal in which activities were conducted at home and people avoided congregation in order to prevent the spread of an infectious disease. This study evaluated the long-term air quality improvement which resulted from the restrictions enforced on normal human activities in Thailand. The air quality index (AQI) of six criteria pollutants and health risk assessments were evaluated in four areas, including metropolitan, suburban, industrial, and tourism areas in Thailand. The results showed that, after the restriction measures, the overall AQI improved by 30%. The subindex of each pollutant (sub-AQI) of most pollutants significantly improved (by 30%) in metropolitan areas after human activities changed due to the implementation of lockdown measures. With regard to industrial and tourism areas, only the sub-AQI of traffic-related pollutants decreased (34%) while the sub-AQIs of other pollutants before and after lockdown were similar. However, the changes in human activities were not clearly related to air quality improvement in the suburban area. The overall hazard index (HI) after lockdown decreased by 23% because of the reduction of traffic-related pollutants. However, the HI value remained above the recommended limits for the health of the adult residents in all areas. Therefore, strict regulations to control other pollutant sources, such as industry and open burning, will also be necessary for air quality improvement in Thailand.

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Natural and anthropogenic disasters are associated with air quality concerns due to the potential redistribution of pollutants in the environment. Our objective was to conduct a spatiotemporal analysis of air concentrations of benzene, toluene, ethylbenzne, and xylene (BTEX) and criteria air pollutants in North Carolina during and after Hurricane Florence. Three sampling campaigns were carried out immediately after the storm (September 2018) and at four-month intervals. BTEX were measured along major roads. Concurrent criteria air pollutant concentrations were predicted from modeling. Correlation between air pollutants and possible point sources was conducted using spatial regression. Exceedances of ambient air criteria were observed for benzene (in all sampling periods) and PM2.5 (mostly immediately after Florence). For both, there was an association between higher concentrations and fueling stations, particularly immediately after Florence. For other pollutants, concentrations were generally below levels of regulatory concern. Through characterization of air quality under both disaster and "normal" conditions, this study demonstrates spatial and temporal variation in air pollutants. We found that only benzene and PM2.5 were present at levels of potential concern, and there were localized increases immediately after the hurricane. These substances warrant particular attention in future disaster response research (DR2) investigations.

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BACKGROUND: Data from extensive mobile measurements (MM) of air pollutants provide spatially resolved information on pedestrians' exposure to particulate matter (black carbon (BC) and PM2.5 mass concentrations). OBJECTIVE: We present a distributional regression model in a Bayesian framework that estimates the effects of spatiotemporal factors on the pollutant concentrations influencing pedestrian exposure. METHODS: We modeled the mean and variance of the pollutant concentrations obtained from MM in two cities and extended commonly used lognormal models with a lognormal-normal convolution (logNNC) extension for BC to account for instrument measurement error. RESULTS: The logNNC extension significantly improved the BC model. From these model results, we found local sources and, hence, local mitigation efforts to improve air quality, have more impact on the ambient levels of BC mass concentrations than on the regulated PM2.5. SIGNIFICANCE: Firstly, this model (logNNC in bamlss package available in R) could be used for the statistical analysis of MM data from various study areas and pollutants with the potential for predicting pollutant concentrations in urban areas. Secondly, with respect to pedestrian exposure, it is crucial for BC mass concentration to be monitored and regulated in areas dominated by traffic-related air pollution.

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The lockdown imposed in Delhi, due to the second wave of the COVID-19 pandemic has led to significant gains in air quality. Under the lockdown, restrictions were imposed on movement of people, operation of industrial establishments and hospitality sector amongst others. In the study, Air Quality Index and concentration trends of six pollutants, i.e. PM2.5, PM10, NO2, SO2, CO, and O3 were analysed for National Capital Territory of Delhi, India for three periods in 2021 (pre-lockdown: 15 March to 16 April 2021, lockdown: 17 April to 31 May 2021 and post-lockdown: 01 June to 30 June). Data for corresponding periods in 2018-2020 was also analysed. Lockdown period saw 6 days in satisfactory AQI category as against 0 days in the same category during the pre-lockdown period. Average PM2.5, PM10, NO2 and SO2 concentrations reduced by 22%, 31%, 25% and 28% respectively during lockdown phase as compared to pre-lockdown phase, while O3 was seen to increase. Variation in meteorological parameters and correlation of pollutants has also been examined. The significant improvement arising due to curtailment of certain activities in the lockdown period indicates the importance of local emission control, and helps improve the understanding of the dynamics of air pollution, thus highlighting policy areas to regulatory bodies for effective control of air pollution.

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Abstract: During the COVID-19 pandemic, restrictive measures are taken by several cities around the world, as well as Rio de Janeiro, reducing routine activities in large urban centers and primary pollutant emissions. This study aims to assess air quality during this partial lockdown through O3, CO, and PM10 concentrations and meteorological data collected in five air quality monitoring stations spread over the whole city, considering the substantial changes in city routine. The period evaluated starts in March 2020, when the partial lockdown was decreed, and ends in September 2020, when economic opening ended. Compared with 2019 data, CO concentration reduced significantly, as expected since the main source of these pollutants is vehicular traffic. O3 concentration increased, most probably as a consequence of the reduction in primary pollutants. On the other hand, PM10 concentration did not vary significantly. From June to September, pollutant concentrations increased responding to the economic opening. Thereby, the partial lockdown contributed to improving air quality in Rio de Janeiro City, which means that changes in work format may be an alternative to reduce atmospheric pollution in big cities, since home office contributes to mobility reductions, and consequently to vehicular emissions. Highlights: • Lockdown contributed to CO reduction and O3 increase.• Differences on rain profile explain low variation on PM10 concentrations.• Lockdown has been like a very long weekend concerning atmospheric pollution.• Home office and distance learning improve air quality. Supplementary Information: The online version contains supplementary material available at 10.1007/s11869-021-01127-2.

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BACKGROUND: The COVID-19 pandemic has had a deep global impact, not only from a social and economic perspective, but also with regard to human health and the environment. To restrict transmission of the virus, the Indian government enforced a complete nationwide lockdown except for essential services and supplies in phases from 25 March to 31 May 2020. Ambient air quality in and around New Delhi, one of the most polluted cities of world, was also impacted during this period. OBJECTIVE: The aim of the present study was to assess and understand the impact of four different lockdown phases (LD1, LD2, LD3 and LD4) on five air pollutants (particulate matter (PM) PM2.5, PM10, nitrogen oxide (NOx), sulfur dioxide (SO2) and ozone (O3)) compared to before lockdown (BLD) at 13 air monitoring stations in and around New Delhi. METHODS: Secondary data on five criteria pollutants for 13 monitoring stations in and around New Delhi for the period 1 March to 31 May 2020 was accessed from the Central Pollution Control Bard, New Delhi. Data were statistically analyzed across lockdown phases, meteorological variables, and prevailing air sources around the monitoring stations. RESULTS: Pollutant concentrations decreased during LD1 compared to BLD except for O3 at all stations. PM2.5 and PM10 remained either close to or higher than the National Ambient Air Quality Standards (NAAQS) due to prevailing high-speed winds. During lockdown phases, NO2 decreased, whereas O3 consistently increased at all stations. This was a paradoxical situation as O3 is formed via photochemical reactions among NOx and volatile organic compounds. Principal component analysis (PCA) extracted two principal components (PC1 and PC2) which explained up to 80% of cumulative variance in data. PM2.5, PM10 and NO2 were associated with PC1, whereas PC2 had loadings of either O3 only or O3 and SO2 depending upon monitoring station. CONCLUSIONS: The present study found that air pollutants decreased during lockdown phases, but these decreases were specific to the site(s) and pollutant(s). The decrease in pollutant concentrations during lockdown could not be attributed completely to lockdown conditions as the planetary boundary layer increased two-fold during lockdown compared to the BLD phase. Such restrictions could be applied in the future to control air pollution but should be approached with caution. COMPETING INTERESTS: The authors declare no competing financial interests.

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Since early 2020, the world has faced an unprecedented pandemic caused by the novel COVID-19 virus. In this study, we characterize the impact of the lockdown associated with the pandemic on air quality in six major cities across the state of Florida, namely: Jacksonville, Tallahassee, Gainesville, Orlando, Tampa, and Miami. Hourly measurements of PM2.5, ozone, NO2, SO2, and CO were provided by the US EPA at thirty sites operated by the Florida Department of Environmental Protection during mid-February to mid-April from 2015 through 2020. To analyze the effect of the pandemic, atmospheric pollutant concentrations in 2020 were compared to historic data at these cities during the same period from 2015 to 2019. Reductions in NO2 and CO levels were observed across the state in most cities and were attributed to restrictions in mobility and the decrease in vehicle usage amid the lockdown. Likewise, decreases in O3 concentrations were observed and were related to the prevailing NOx-limited regime during this time period. Changes in concentrations of SO2 exhibited spatial variations, concentrations decreased in northern cities, however an increase was observed in central and southern cities, likely due to increased power generation at facilities primarily in the central and southern regions of the state. PM2.5 levels varied temporally during the study and were positively correlated with SO2 concentrations during the lockdown. In March, reductions in PM2.5 levels were observed, however elevations in PM2.5 concentrations in April were attributed to long-range transport of pollutants rather than local emissions. This study provides further insight into the impacts of the COVID-19 pandemic on anthropogenic sources from vehicular emissions and power generation in Florida. This work has implications for policies and regulations of vehicular emissions as well as consequences on the use of sustainable energy sources in the state.

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Air quality has been significantly improved in China in recent years; however, our knowledge of the long-term changes in health risks from exposure to air pollutants remain less understood. Here we investigated the temporal variations and spatial distributions of six criteria pollutants (SO2, NO2, O3, CO, PM2.5 and PM10) in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) during 2015-2019. SO2 showed 36-60% reductions in three regions, comparatively, NO2 decreased by 3-17% in BTH and YRD and had a 5% increase in PRD. PM2.5 and PM10 showed the largest reductions in BTH (30-33%) and the lowest in PRD (7-13%), while O3 increased by 9% during 2015-2019 particularly in BTH and YRD. Assuming that only air pollutants above given thresholds exert excess risk (ERtotal) of mortality, we found that the different variations of pollutants have caused ERtotal in BTH decreasing significantly from 4.8% in 2015 to 2.0% in 2019, while from 1.9% to 1.0% in YRD, and a small change in PRD. These results indicate substantially decreased health risks of mortality from exposure to air pollutants as a response to improved air quality. Overall, PM2.5 dominated ERtotal accounting for 42-53% in BTH and 58-64% in YRD with steadily increased contributions, yet ERtotal presented strong seasonal dependence on air pollutants with largely increased contribution of O3 in summer. The ERtotal caused by SO2 was decreased substantially and became negligible except in winter in BTH, while NO2 only played a role in winter. We also found that ERPM2.5 was compositional dependent with organics being the major contributor at low ERPM2.5 while nitrate was more important at high ERPM2.5. Our results highlight that evaluation of public health risks of air pollution needs to consider chemical differences of PM in different regions in addition to dominant air pollutants in different seasons.

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ABSTRACT: The COVID-19 lockdown has not only helped in combating the community transmission of SARS-CoV-2 but also improved air quality in a very emphatic manner in most of the countries. In India, the first phase of COVID-19 lockdown came into force on March 25, 2020, which was later continued in the next phases. The purpose of this study was to investigate the result of lockdown on air quality of major metropolitan cities-Delhi, Mumbai, Kolkata, Chennai, Bengaluru, Hyderabad, Jaipur, and Lucknow-from March 25 to May 3, 2020. For this study, the concentration of six criteria air pollutants (PM2.5, PM10, CO, NO2, SO2, and O3) and air quality index during the COVID-19 lockdown period was compared with the same period of the previous year 2019. The results indicate a substantial improvement in air quality with a drastic decrease in the concentration of PM2.5, PM10, CO, and NO2, while there is a moderate reduction in SO2 and O3 concentration. During the lockdown period, the maximum reduction in the concentration of PM2.5, PM10, CO, NO2, SO2, and O3 was observed to be - 49% (Lucknow), - 57% (Delhi), - 75% (Mumbai), - 68% (Kolkata), - 48% (Mumbai), and - 29% (Hyderabad), respectively. The value of the air quality index (AQI) also dwindled significantly during the COVID-19 lockdown period. The maximum decline in AQI was observed - 52% in Bengaluru and Lucknow. The order of AQI was satisfactory > moderate > good > poor and the frequency order of prominent pollutants was O3 > PM10 > PM2.5 > CO > NO2 > SO2 during the lockdown period in all the aforementioned metropolitan cities.

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Exposure to fine particulate matter (PM2.5) is associated with increased mortality. Although epidemiology studies typically use outdoor PM2.5 concentrations as surrogates for exposure, the majority of PM2.5 exposure in the US occurs in microenvironments other than outdoors. We develop a framework for estimating the total US mortality burden attributable to exposure to PM2.5 of both indoor and outdoor origin in the primary non-smoking microenvironments in which people spend most of their time. The framework utilizes an exposure-response function combined with adjusted mortality effect estimates that account for underlying exposures to PM2.5 of outdoor origin that likely occurred in the original epidemiology populations from which effect estimates are derived. We demonstrate the framework using several different scenarios to estimate the potential magnitude and bounds of the US mortality burden attributable to total PM2.5 exposure across all non-smoking environments under a variety of assumptions. Our best estimates of the US mortality burden associated with total PM2.5 exposure in the year 2012 range from ~230,000 to ~300,000 deaths. Indoor exposure to PM2.5 of outdoor origin is typically the largest total exposure, accounting for ~40-60% of total mortality, followed by residential exposure to indoor PM2.5 sources, which also drives the majority of variability in each scenario.

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In epidemiologic studies of health effects of air pollution, measurements or models are used to estimate exposure. Exposure estimates have errors that propagate to effect estimates in exposure-response models. We critically evaluate how types of exposure measurement error influenced bias and precision of effect estimates to understand conditions affecting interpretation of exposure-response models for epidemiologic studies of exposure to PM2.5, NO2, and SO2. We reviewed available literature on exposure measurement error for time-series and long-term exposure epidemiology studies. For time-series studies, time-activity error (daily exposure concentration did not account for variation in exposure due to time-activity during a day) and nonambient (indoor) sources negatively biased the effect estimates and increased standard error, so uncertainty grew with increasing bias while underestimating the true health effect in these studies. Spatial error (deviation between true exposure concentration at an individual's location and concentration at a receptor) was ascribed to negatively biased effect estimates in most cases. Positive bias occurred for spatially variable pollutants when the variance of error correlated with the exposure estimate. For long-term exposure studies, most spatial errors did not bias the effect estimate. For both time-series and long-term exposure studies reviewed, large uncertainties were observed when exposure concentration was modeled with low spatial and temporal resolution for a spatially variable pollutant.

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The development of air quality management (AQM) strategies provides opportunities to improve public health and reduce health inequalities. This study evaluates health and inequality impacts of alternate SO2 control strategies in Detroit, MI, a designated non-attainment area. Control alternatives include uniform reductions across sources, ranking approaches based on total emissions and health impacts per ton of pollutant emitted, and optimizations that meet concentration and health goals. Using dispersion modeling and quantitative health impact assessment (HIA), these strategies are evaluated in terms of ambient concentrations, health impacts, and the inequality in health risks. The health burden attributable to SO2 emissions in Detroit falls primarily among children and includes 70 hospitalizations and 6,000 asthma-related respiratory symptom-days annually, equivalent to 7 disability-adjusted life years (DALYs). The health burden disproportionately falls on Hispanic/Latino residents, residents with less than a high school diploma, and foreign-born residents. Control strategies that target smaller facilities near exposed populations provide the greatest benefit in terms of the overall health burden reductions and the inequality of attributable health risk; conventional strategies that target the largest emission sources can increase inequality and provide only modest health benefits. The assessment is novel in using spatial analyses that account for urban scale gradients in exposure, demographics, vulnerability, and population health. We show that quantitative HIA methods can be used to develop AQM strategies that simultaneously meet environmental, public health, and environmental justice goals, advancing AQM beyond its current compliance-oriented focus.

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The detrimental effects of environmental pollution on one's health are undeniable and have been demonstrated time and time again. Breathing in pollutants in ambient air often has consequences throughout the body, including cardiovascular disease, effects on the reproductive system, and oncologic implications. In the respiratory system, chronic exposure yields a number of outcomes, including chronic obstructive pulmonary disease (COPD) and asthma exacerbations, increased rates of hospitalizations, and increased severity of acute illnesses. On a macro-level, this morbidity and mortality then leads to vast and far-reaching public health consequences the world over, including the loss of billions of dollars' worth of labor. This is especially applicable in developing countries, which often undergo rapid growth, industrialization and urbanization with a resultant increase in vehicular traffic, coal combustion, and fuel emissions as a whole. For this reason, environmental pollutants have been studied extensively, and countries around the globe have established laws that regulate ambient air levels of so-called criteria pollutants. This article will explore several of these criteria pollutants, including particulate matter, nitrogen dioxide, sulfur dioxide, and ozone, and their individual relationships to asthma pathophysiology. However, it is also emphasized that though each one of these toxins yields its own effects, the group of them often works together to have cumulative consequences. For these reasons and many more, it is important to remain aware and educated about these omnipresent environmental pollutants.

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Sutton's Law urges the medical practitioner to utilize the test that goes directly to the problem. When applied to exposure science, Sutton's Law would argue that the major emphasis should be on techniques that directly measure exposure in or close to the human, animal or ecosystem receptors of concern. Exposure science largely and appropriately violates Sutton's Law by estimating exposure based on information on emissions or measurements obtained at a distance from the receptors of concern. I suggest four criteria to help determine whether Sutton's law should be violated for an innovative technology, and explore these criteria in relation to potential human exposure resulting from unconventional gas drilling (UGD): (1) The technological processes possibly leading to release of the chemical or physical agents of concern are reasonably understood; (2) the agents of concern are known; (3) the source and geographical location of the releases can be reasonably identified; and (4) there is information about the likely temporal pattern of the releases and resulting pollutant levels in relation to the temporal patterns of receptor susceptibility. For UGD, the complexity of the technology including many possible release points at different time periods; the existence of three variable mixtures of chemical and physical agents as well as possible unknown reactants; the demonstrated large variation in releases from site to site; and deficiencies in transparency and regulatory oversight, all suggest that studies of the potential health impact of UGD should follow Sutton's Law. This includes the use of techniques that more directly measure exposure close to or within the receptors of concern, such as biological markers or through community-based citizen science. Understanding the implications of Sutton's Law could help focus scientific and regulatory efforts on effective approaches to evaluate the potential health and ecosystem implications of new and evolving technologies.

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