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1. Mapping Social Vulnerability to Air Pollution in Philadelphia, PA

   Conway, Meghan (September 2020, Veritas)

   null (Ed.)

   Environmental stresses borne of population growth, consumerism and industrialization have subjected many populations worldwide to elevated air pollution. Philadelphia, a historically industrial city in Northeastern United States, is ranked in the top 25 cities in the country for harmful air pollutants (PM2.5, ozone). Philadelphia also experiences 􀁋􀁖􀁉􀁅􀁘􀀄 􀆼􀁒􀁅􀁒􀁇􀁍􀁅􀁐􀀄 􀁗􀁘􀁖􀁅􀁘􀁍􀆼􀁇􀁅􀁘􀁍􀁓􀁒􀀄 􀁅􀁒􀁈􀀄 􀁉􀁒􀁚􀁍􀁖􀁓􀁒􀁑􀁉􀁒􀁘􀁅􀁐􀀄 􀁖􀁅􀁇􀁍􀁗􀁑􀀐􀀄 􀁛􀁌􀁍􀁇􀁌􀀄 􀁓􀁊􀁘􀁉􀁒􀀄 􀁙􀁒􀁊􀁅􀁍􀁖􀁐􀁝􀀄 􀁅􀁗􀁗􀁉􀁖􀁘􀁗􀀄 􀁘􀁌􀁉􀀄 􀁔􀁅􀁍􀁒􀁗􀀄 􀁓􀁊􀀄 􀁉􀁒􀁚􀁍􀁖􀁓􀁒􀁑􀁉􀁒􀁘􀁅􀁐􀀄 pollution & associated health effects on socioeconomically disadvantaged communities. This study seeks to succinctly 􀁕􀁙􀁅􀁒􀁘􀁍􀁊􀁝􀀄􀁛􀁌􀁍􀁇􀁌􀀄􀁔􀁓􀁔􀁙􀁐􀁅􀁘􀁍􀁓􀁒􀁗􀀄􀁑􀁅􀁝􀀄􀁆􀁉􀀄􀁅􀁘􀀄􀁖􀁍􀁗􀁏􀀄􀁊􀁓􀁖􀀄􀁌􀁉􀁅􀁐􀁘􀁌􀀄􀁉􀁊􀁊􀁉􀁇􀁘􀁗􀀄􀁅􀁗􀁗􀁓􀁇􀁍􀁅􀁘􀁉􀁈􀀄􀁛􀁍􀁘􀁌􀀄􀁅􀁍􀁖􀀄􀁔􀁓􀁐􀁐􀁙􀁘􀁍􀁓􀁒􀀄􀀌􀁗􀁔􀁉􀁇􀁍􀆼􀁇􀁅􀁐􀁐􀁝􀀄􀁅􀁗􀁘􀁌􀁑􀁅􀀐􀀄􀁇􀁌􀁖􀁓􀁒􀁍􀁇􀀄 obstructive pulmonary disease) through a suite of census-derived attributes. Using ArcMap Geographical Information System software (ESRI), attributes, categorized as promoting vulnerability or adaptability, are combined with air pollution data collected in summer 2019 to form a non-weighted ‘Social Vulnerability Index’ (SVI) at a census-tract level for Philadelphia. SVI demonstrated several clusters of neighborhoods with great disparities in socioeconomic factors. The census tracts with higher SVI tended to have higher levels of asthma and COPD (and vice versa). With improvements and acknowledgement of Philadelphia’s uniqueness, SVI of this kind may be used to inform policymakers on city planning (e.g. placement of future highways, industrial centers, etc.) to alleviate compounded respiratory/ pulmonary-related stresses on disadvantaged communities. Future analysis including green space coverage, other 􀁊􀁓􀁖􀁑􀁗􀀄􀁓􀁊􀀄􀁅􀁍􀁖􀀄􀁔􀁓􀁐􀁐􀁙􀁘􀁍􀁓􀁒􀀐􀀄􀁅􀁒􀁈􀀓􀁓􀁖􀀄􀁅􀀄􀁕􀁙􀁅􀁒􀁘􀁍􀆼􀁇􀁅􀁘􀁍􀁓􀁒􀀄􀁓􀁊􀀄􀁗􀁓􀁇􀁍􀁅􀁐􀀄􀁇􀁓􀁒􀁒􀁉􀁇􀁘􀁍􀁚􀁍􀁘􀁝􀀄􀁑􀁅􀁝􀀄􀁌􀁉􀁐􀁔􀀄􀁘􀁓􀀄􀁍􀁑􀁔􀁖􀁓􀁚􀁉􀀄􀁊􀁙􀁖􀁘􀁌􀁉􀁖􀀄􀁙􀁒􀁈􀁉􀁖􀁗􀁘􀁅􀁒􀁈􀁍􀁒􀁋􀀄􀁓􀁊􀀄􀁘􀁌􀁉􀀄 intersection between socioeconomic factors, air pollution, and health in Philadelphia, PA. 

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2. Impacts of sectoral, regional, species, and day-specific emissions on air pollution and public health in Washington, DC

   https://doi.org/10.1525/elementa.2021.00043  

   Nawaz, M. O.; Henze, D. K.; Harkins, C.; Cao, H.; Nault, B.; Jo, D.; Jimenez, J.; Anenberg, S. C.; Goldberg, D. L.; Qu, Z. (January 2021, Elementa: Science of the Anthropocene)

   We present a novel source attribution approach that incorporates satellite data into GEOS-Chem adjoint simulations to characterize the species-specific, regional, and sectoral contributions of daily emissions for 3 air pollutants: fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2). This approach is implemented for Washington, DC, first for 2011, to identify urban pollution sources, and again for 2016, to examine the pollution response to changes in anthropogenic emissions. In 2011, anthropogenic emissions contributed an estimated 263 (uncertainty: 130–444) PM2.5- and O3-attributable premature deaths and 1,120 (391–1795) NO2 attributable new pediatric asthma cases in DC. PM2.5 exposure was responsible for 90% of these premature deaths. On-road vehicle emissions contributed 51% of NO2-attributable new asthma cases and 23% of pollution-attributable premature deaths, making it the largest contributing individual sector to DC’s air pollution–related health burden. Regional emissions, originating from Maryland, Virginia, and Pennsylvania, were the most responsible for pollution-related health impacts in DC, contributing 57% of premature deaths impacts and 89% of asthma cases. Emissions from distant states contributed 34% more to PM2.5 exposure in the wintertime than in the summertime, occurring in parallel with strong wintertime westerlies and a reduced photochemical sink. Emission reductions between 2011 and 2016 resulted in health benefits of 76 (28–149) fewer pollution-attributable premature deaths and 227 (2–617) fewer NO2-attributable pediatric asthma cases. The largest sectors contributing to decreases in pollution-related premature deaths were energy generation units (26%) and on-road vehicles (20%). Decreases in NO2-attributable pediatric asthma cases were mostly due to emission reductions from on-road vehicles (63%). Emission reductions from energy generation units were found to impact PM2.5 more than O3, while on-road vehicle emission reductions impacted O3 proportionally more than PM2.5. This novel method is capable of capturing the sources of urban pollution at fine spatial and temporal scales and is applicable to many urban environments, globally. 

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3. Mobile Monitoring of Air Pollution Reveals Spatial and Temporal Variation in an Urban Landscape

   https://doi.org/10.3389/fbuil.2021.648620  

   Cummings, Lucas E.; Stewart, Justin D.; Reist, Radley; Shakya, Kabindra M.; Kremer, Peleg (May 2021, Frontiers in Built Environment)

   null (Ed.)

   Urban air pollution poses a major threat to human health. Understanding where and when urban air pollutant concentrations peak is essential for effective air quality management and sustainable urban development. To this end, we implement a mobile monitoring methodology to determine the spatiotemporal distribution of particulate matter (PM) and black carbon (BC) throughout Philadelphia, Pennsylvania and use hot spot analysis and heatmaps to determine times and locations where pollutant concentrations are highest. Over the course of 12 days between June 27 and July 29, 2019, we measured air pollution concentrations continuously across two 150 mile (241.4 km) long routes. Average daily mean concentrations were 11.55 ± 5.34 μg/m 3 (PM 1 ), 13.48 ± 5.59 μg/m 3 (PM 2.5 ), 16.13 ± 5.80 μg/m 3 (PM 10 ), and 1.56 ± 0.39 μg/m 3 (BC). We find that fine PM size fractions (PM 2.5 ) constitute approximately 84% of PM 10 and that BC comprises 11.6% of observed PM 2.5 . Air pollution hotspots across three size fractions of PM (PM 1 , PM 2.5 , and PM 10 ) and BC had similar distributions throughout Philadelphia, but were most prevalent in the North Delaware, River Wards, and North planning districts. A plurality of detected hotspots found throughout the data collection period (30.19%) occurred between the hours of 8:00 AM–9:00 AM. 

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4. Impacts of climate change on future air quality and human health in China

   https://doi.org/10.1073/pnas.1812881116  

   Hong, Chaopeng; Zhang, Qiang; Zhang, Yang; Davis, Steven J.; Tong, Dan; Zheng, Yixuan; Liu, Zhu; Guan, Dabo; He, Kebin; Schellnhuber, Hans Joachim (August 2019, Proceedings of the National Academy of Sciences)

   In recent years, air pollution has caused more than 1 million deaths per year in China, making it a major focus of public health efforts. However, future climate change may exacerbate such human health impacts by increasing the frequency and duration of weather conditions that enhance air pollution exposure. Here, we use a combination of climate, air quality, and epidemiological models to assess future air pollution deaths in a changing climate under Representative Concentration Pathway 4.5 (RCP4.5). We find that, assuming pollution emissions and population are held constant at current levels, climate change would adversely affect future air quality for >85% of China’s population (∼55% of land area) by the middle of the century, and would increase by 3% and 4% the population-weighted average concentrations of fine particulate matter (PM2.5) and ozone, respectively. As a result, we estimate an additional 12,100 and 8,900 Chinese (95% confidence interval: 10,300 to 13,800 and 2,300 to 14,700, respectively) will die per year from PM2.5 and ozone exposure, respectively. The important underlying climate mechanisms are changes in extreme conditions such as atmospheric stagnation and heat waves (contributing 39% and 6%, respectively, to the increase in mortality). Additionally, greater vulnerability of China’s aging population will further increase the estimated deaths from PM2.5 and ozone in 2050 by factors of 1 and 3, respectively. Our results indicate that climate change and more intense extremes are likely to increase the risk of severe pollution events in China. Managing air quality in China in a changing climate will thus become more challenging. 

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5. Data Augmentation Strategies for Improved PM2.5 Forecasting Using Transformer Architectures

   https://doi.org/10.3390/atmos16020127  

   Pan, Phoebe; Malarvizhi, Anusha Srirenganathan; Yang, Chaowei (February 2025, Atmosphere)

   Breathing in fine particulate matter of diameter less than 2.5 µm (PM2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m3 and 35.5 µg/m3) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m3 threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R2 of 0.914, particularly excelling in forecasting high PM2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins. 

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