This study analyzed speciated PM data (2014-2024) from the Chemical Speciation Network in central Los Angeles and used positive matrix factorization (PMF) to identify and apportion sources, and quantified changes in their contributions over time to examine combined effects of regulatory actions, COVID-19 Lockdowns, and Wildfire Episodes over this period. Nine factors were identified, namely vehicular emissions, tire wear, mineral dust, fresh sea salt, aged sea salt + marine combustion, biomass burning, secondary nitrate, secondary sulfate, and secondary organic aerosol (SOA). Over the decade, mean gravimetric PM declined from 15.4 μg/m to 10.5 μg/m (-34 %), driven primarily by a statistically significant 47% decrease in vehicular emissions (from 2.15 to 1.14 μg/m), while secondary nitrate also declined by 61% (from 5.57 to 2.17 μg/m), though this trend was not statistically significant; secondary sulfate remained steady at 0.83-1.38 μg/m. Considering the tightening of the annual PM standard to 9.0 μg/m, central Los Angeles remains in nonattainment, underscoring the need for additional mitigation strategies to drive the annual mean below this revised threshold. Moreover, two significant perturbations interrupted these trends: stay-at-home orders (March-June 2020) suppressed vehicular and nitrate contributions by roughly 65 %, although meteorological conditions and data limitations might introduce some uncertainty, while the Bobcat Fire later in 2020 doubled the annual biomass-burning contribution to 3.64 μg/m (peaking at 6.78 μg/m in September) and raised SOA to 2.47 μg/m. Collectively, the results highlight the interplay between sustained emission controls and natural events, offering guidance for adaptive air-quality management.

Exposure to fine particulate matter (PM) from woodsmoke is a national and global public health concern. While wood heat use is increasing in the Northeast U.S., exposure to woodsmoke in rural valleys in this region remains understudied. Low-cost sensors have recently emerged as a promising strategy to better assess PM exposure in communities across the globe. However, real-world, long-term community air monitoring studies deploying low-cost sensors are lacking. Such studies are necessary to validate performance over time for the goals of assessing exposure trends and providing practical guidance to future community-scale projects. Here, we evaluated PM community-wide exposure over four years in a rural, woodsmoke impacted community that deployed a Purple Air network. We determined significant differences between the PM regulatory reference monitor and Purple Air PM concentrations across the community, over multiple heating seasons, at both hourly ( < 0.01) and 8-h average time intervals ( < 0.001). PM was especially elevated in the evenings (6 p. m.-2 a.m.), with a maximum 1-h average of 81.7 μg/m and a maximum 8-h average of 78.7 μg/m. Consistent with other performance evaluations in the literature, we determined Purple Air sensors have low bias after correction (2.4 % Normalized Mean Bias Error [], 3.3 μg/m Root Mean Square Error []). Co-located sensors were reliable in harsh winter conditions for up to three years. We suggest quality assurance, data management, correction model selection, and citizen science partnerships are critical considerations for sensor network deployments aiming to assess long-term exposure and health.

Saint John experiences ambient sulphur dioxide (SO) pollution due to a high density of industrial activities. Despite recent reduction in SO emissions, over 90% of the provincial exceedances of air pollutants were related to SO or total reduced sulphur (TRS), and over 70% among which occurred in Saint John. Pinpointing intra-urban SO hot spots is important for revealing the neighborhoods exposed to high health risk. However, this is challenging due to limited spatial coverage of monitoring. To fill the monitoring gap, we developed two-stage gradient boosting models combining a classifier that discerned between SO-free and SO-polluted days and a regressor that estimated daily SO levels based on remote sensing data. With a 10-fold cross-validation, the classifier achieved 83% accuracy and the regressors attained R of 0.46 and 0.44 for daily mean and maximum SO respectively. Based on model outputs, we conducted spatial hot spot analysis and found high SO levels spread to northeast, northwest, and southeast Saint John, where SO monitoring was absent. Several existing monitoring sites in west Saint John coincided with the hot spots, where SO was not regularly measured. Besides the spatiotemporal lags of nearby monitored SO, wind-related variables such as wind speed and direction had high importance in predicting surface SO, which might suggest potential impacts to remote unmonitored communities from the transport of SO. In summary, our findings suggest that certain unmonitored areas in Saint John may experience high SO levels. Expansion of monitoring efforts would help inform where and when mitigation should be taken to minimize SO-related health impacts.

Understanding the sources and formation processes of fine particulate matter (PM) is crucial for improving urban air quality and public health. This study provides a real-time analysis of PM-bound water-soluble organic carbon (WSOC) and related carbonaceous species during winter, spring, and summer periods in 2023-2024, aiming to identify their major sources in central Los Angeles. Using advanced online monitoring equipment, including a Sunset Laboratory EC/OC analyzer and a custom-developed setup including a total organic carbon (TOC) analyzer coupled with a particle collection system, we obtained hourly measurements of organic carbon (OC), its fractions (OC-OC, based on volatility), elemental carbon (EC), and WSOC. Positive matrix factorization (PMF) identified three principal PM sources: vehicular emissions, secondary organic carbon (SOC) formation influenced by nighttime aqueous-phase chemical processes, and SOC formation driven by daytime photochemical reactions. Vehicular emissions dominated EC levels, accounting for 86-95% across seasons. This factor also had high contributions from nitrogen oxides (NOₓ) (75-82%), vehicle counts (approximately 85%), and OC (51-83%), reflecting the persistent influence of traffic emissions. Nighttime SOC formation was significant in winter, with WSOC and OC contributing 58% and 40% to this factor. In contrast, daytime photochemical SOC formation was prominent in summer, with WSOC and OC contributing 63% and 47%, and ozone loading up to 89%, reflecting increased photochemical activity. Spring exhibited a mix of aqueous and photochemical SOC formation, with similar contributions from WSOC (38-35%) and OC (35-33%), reflecting the transitional season's mixed SOC formation mechanisms. Diurnal profiles revealed that primary emissions peaked during morning rush hours, while secondary formation processes elevated OC levels at night in winter and during afternoons in summer. The EC tracer method corroborated these findings by estimating primary and secondary organic carbon levels, highlighting significant seasonal and diurnal variations in carbonaceous aerosols. These results emphasize the need for targeted strategies addressing both primary emissions and the precursors of secondary aerosol formation, to improve air quality in Los Angeles.

Ground-level ozone (O) pollution has shifted from a scientific issue to a key focus of governmental action in China. In recent years, the concentration of NO in Shanghai has shown a decreasing trend of 3.7% annually, but ozone concentrations have exhibited significant interannual variability, particularly with a noticeable increase in 2022. This study focuses on investigating the mechanisms behind the increase in ozone concentration during the COVID-19 pandemic control period in 2022 in Shanghai, utilizing a combination of ground observation data, observation-based models, and chemical transport models for analysis. The results indicate that during the lockdown period, the MDA8 in Shanghai increased by 17 μg/m compared to before, with emission-related factors contributing 65.3%, primarily due to a blanket reduction in VOCs and NOx emissions during the lockdown, with a reduction ratio close to 1:1. However, this reduction ratio and intensity are not sufficiently reasonable to alleviate ozone pollution. Meanwhile, adverse meteorological conditions further exacerbated this effect, contributing 34.7%, with temperature rise having the greatest impact. Results from the chemical transport model show that with the total reduction in NOx and VOCs emissions unchanged, the greater the reduction in VOCs emissions, the better the reduction effect on ozone pollution, reducing MDA8 O by approximately 10 μg/m, especially for the control of reactive compounds such as alkenes, aromatics, and OVOCs. However, if the reduction ratio of NOx is greater than that of VOCs, ozone concentrations may not decrease but instead increase. This indicates that ozone concentration is influenced not only by the intensity of emissions reduction but also by the ratio of emissions reduction between NOx and VOCs. Our study emphasizes the critical role of carefully designed strategies, focusing on controlling the ratio of VOCs to NOx and increasing the intensity of VOCs reduction, to effectively alleviate ozone pollution in urban areas.

Chlorine radicals (Cl) play a crucial role in atmospheric chemistry. The COVID-19 (coronavirus disease 2019) pandemic significantly increased the use of chlorine-containing disinfectants in China, leading to enhanced emissions of chlorine gas (Cl) and hypochlorous acid (HOCl). In this study, we use the Community Multiscale Air Quality (CMAQ) model with updated chlorine chemistry to evaluate the potential air quality impact of chlorine emissions from disinfectant usage during February 2020, a month with a large outbreak of COVID-19 in mainland China. Results indicate that during the pandemic, there was a sharp increase of reactive chlorine emissions, with Cl and HOCl emissions reaching 773.9 t and 5,913.1 t, making 3 times increase. The emissions of chlorine enhanced atmospheric oxidation capacity (AOC) through the oxidation of VOC by Cl and OH, with the average enhancement in Shanghai, Beijing and Wuhan areas reaching 4.6% ± 1.8%, 10.3% ± 6.6% and 7.4% ± 4.6%, respectively. Consequently, the use of chlorine-containing disinfectants may have contributed to observed increases in the monthly average of the maximum daily 8-h average ozone (MDA8 O) and fine particulate matter (PM) concentrations by up to 1.5 ppbv (5%) and 1.7 μg/m (1%), respectively. Especially, the maximum hourly increase values of O and PM concentrations were 4.8 ppbv and 11.4 μg/m3, 2.5 ppbv and 4.5 μg/m3 in Beijing and Wuhan, respectively. These results indicate that chlorine emissions from the widespread use of disinfectants have had a significant impact on air quality in certain regions (Beijing and Wuhan) and during specific periods (9:00~12:00).

Coal-fired power plants are major contributors of ambient sulfur dioxide (SO) air pollution. Epidemiological literature suggests an adverse association between SO exposure during gestation and preterm birth (PTB; <37 weeks completed gestation). PTB is strongly associated with infant mortality and increased risk for later life morbidities.

The US Environmental Protection Agency (EPA) estimates on-road vehicles emissions using the Motor Vehicle Emission Simulator (MOVES). We developed updated ammonia emission rates for MOVES based on road-side exhaust emission measurements of light-duty gasoline and heavy-duty diesel vehicles. The resulting nationwide on-road vehicle ammonia emissions are 1.8, 2.1, 1.8, and 1.6 times higher than the MOVES3 estimates for calendar years 2010, 2017, 2024, and 2035, respectively, primarily due to an increase in light-duty gasoline vehicle NH emission rates. We conducted an air quality simulation using the Community Multi-Scale Air Quality (CMAQv5.3.2) model to evaluate the sensitivity of modeled ammonia and fine particulate matter (PM) concentrations in calendar year 2017 using the updated on-road vehicle ammonia emissions. The average monthly urban ammonia ambient concentrations increased by up to 2.3 ppb in January and 3.0 ppb in July. The updated on-road NH emission rates resulted in better agreement of modeled ammonia concentrations with 2017 annual average ambient ammonia measurements, reducing model bias by 5.8 % in the Northeast region. Modeled average winter PM concentrations increased in urban areas, including enhancements of up to 0.5 μg/m in the northeast United States. The updated ammonia emission rates have been incorporated in MOVES4 and will be used in future versions of the NEI and EPA's modeling platforms.

The interaction of sunlight with volatile organic compounds (VOCs) emitted from various sources results in mutagenic photooxidation products that contribute substantially to air pollution. Evaporation of gasoline is one such source of VOCs; however, no studies have evaluated the mutagenicity of the photooxidation products of gasoline vapors or of many of the non-aromatic constituent VOCs of gasoline. Here we determined the mutagenicity in TA100 of atmospheres generated in a steady-state atmospheric simulation chamber by irradiating gasoline and individual non-aromatic VOCs in the presence of nitrogen oxides (NO) in air. In addition to gasoline, we evaluated α-pinene; 2-pentene; ethanol; isobutanol; isoprene; and 2,2,4-trimethylpentane (isooctane). Cells were exposed at the air-agar interface to the atmospheres for 1, 2, 4, 8, or 16 h. Atmospheres generated in the dark were not mutagenic. However, under irradiation all atmospheres other than that of 2,2,4-trimethylpentane were mutagenic, with mutagenic potencies spanning 8.6-fold. The mutagenicity was due exclusively to direct-acting, late-generation photooxidation products. The non-aromatic VOCs studied here contributed little to the mutagenic potency of the photooxidation products of gasoline. However, the sum of the mutagenic potencies of these atmospheres plus those from the photooxidation of some aromatic VOCs in gasoline measured here and elsewhere (Riedel et al., Atmos Environ, 178:164, 2018) accounted for 71% of the mutagenic potency of the photooxidation products of gasoline vapor. In photochemical mixtures with strong biogenic contributions, isoprene products may also contribute significantly to mutagenic potency. Strategies to reduce the emissions of gasoline and those VOCs whose photooxidation products are most mutagenic would reduce VOC-associated air pollution and improve public health.

In recent decades, anthropogenic emissions in the Gulf of Mexico have caused a deterioration in air quality in surrounding areas, mainly due to particulate matter, sulfur dioxide and nitrogen dioxide. Over this period, interest in reactive nitrogen compounds has increased due to their relationships to air quality and ecosystem impacts associated with atmospheric deposition. In this study we summarize air concentrations of nitrogen dioxide in ambient air and wet atmospheric deposition of nitrate and ammonium along the southern coast of the United States of America and eastern Mexico over the period 2017 to 2020. The highest concentrations of ammonium and nitrate were observed in precipitation from Texas and sites in Mexico, although the highest rates of atmospheric deposition were observed in Florida and Louisiana. The ratio between ammonium and nitrate in precipitation was higher in Mexican stations than in studied sites in the United States, influenced by meteorological factors, atmospheric emissions, and characteristic air mass transport in the region. Backtrajectory modeling showed the importance of the seasonality of the wind in the transport of nitrogen compounds of local and regional origin that affect Veracruz.

South America is underrepresented in research on air pollution exposure disparities by sociodemographic factors, although such disparities have been observed in other parts of the world. We investigated whether exposure to and information about air pollution differs by sociodemographic factors in the city of Rio de Janeiro, the second most populous city in Brazil with dense urban areas, for 2012-2017. We developed machine learning-based models to estimate daily levels of O, PM, and PM using high-dimensional datasets from satellite remote sensing, atmospheric and land variables, and land use information. Cross-validations demonstrated good agreement between the estimated levels and measurements from ground-based monitoring stations: overall of 76.8 %, 63.9 %, and 69.1 % for O, PM, and PM, respectively. We conducted univariate regression analyses to investigate whether long-term exposure to O, PM, PM and distance to regulatory monitors differs by socioeconomic indicators, the percentages of residents who were children (0-17 years) or age 65+ years in 154 neighborhoods. We also examined the number of days exceeding the Brazilian National Air Quality Standard (BNAQS). Long-term exposures to O and PM were higher in more socially deprived neighborhoods. An interquartile range (IQR) increment of the social development index (SDI) was associated with a 3.6 μg/m (95 % confidence interval [CI]: 2.9, 4.4; p-value≤0.001) decrease in O, and 0.3 μg/m (95 % CI: 0.2, 0.5; p-value = 0.010) decrease in PM. An IQR increase in the percentage of residents who are children was associated with a 4.1 μg/m (95 % CI: 3.1, 5.0; p-value≤0.001) increase in O, and 0.4 μg/m (95 % CI: 0.3, 0.6; p-value = 0.009) increase in PM. An IQR increase in the percentage of residents age ≥65was associated with a 3.3 μg/m (95 % CI: 2.4, 4.3; p-value=<0.001) decrease in O, and 0.3 μg/m (95 % CI: 0.1, 0.5; p-value = 0.058) decrease in PM. There were no apparent associations for PM. The association for daily O levels exceeding the BNAQS daily standard was 0.4 %p-0.8 %p different by the IQR of variables, indicating a 7-15 days difference in the six-year period. The association for daily PM levels exceeding the BNAQS daily standard showed a 0.7-1.5 %p difference by the IQR, meaning a 13-27 days difference in the period. We did not find statistically significant associations between the distance to monitors and neighborhood characteristics but some indication regarding SDI. We found that O levels were higher in neighborhoods situated farther from monitoring stations, suggesting that elevated levels of air pollution may not be routinely measured. Exposure disparity patterns may vary by pollutants, suggesting a complex interplay between environmental and socioeconomic factors in environmental justice.

In this study, we developed two online monitors for total organic carbon (TOC) and water-soluble organic carbon (WSOC) measurements in fine (d < 2.5μm) and coarse (2.5μm < d < 10μm) particulate matter (PM), respectively. Their performance has been evaluated in laboratory and field tests to demonstrate the feasibility of using these monitors to measure near real-time concentrations, with consideration of their potential for being employed in long-term measurements. The fine PM collection setup was equipped with a versatile aerosol concentration enrichment system (VACES) connected to an aerosol-into-liquid-sampler (AILS), whereas two virtual impactors (VIs) in tandem with a modified BioSampler were used to collect coarse PM. These particle collection setups were in tandem with a Sievers M9 TOC analyzer to read TOC and WSOC concentrations in aqueous samples hourly. The average hourly TOC concentration measured by our developed monitors in fine and coarse PM were 5.17 ± 2.41 and 0.92 ± 0.29 μg/m, respectively. In addition, our TOC readings showed good agreement and were comparable with those quantified using Sunset Lab EC/OC analyzer operating in parallel as a reference. Furthermore, we conducted field tests to produce diurnal profiles of fine PM-bound WSOC, which can show the effects of ambient temperature on maximum values in the nighttime chemistry of the winter, as well as on increased photochemical activities in afternoon peaks during the summer. According to our experimental campaign, WSOC mean values during the study period (3.07 μg/m for the winter and 2.7 μg/m for the summer) were in a comparable range with those of earlier studies in Los Angeles. Overall, our results corroborate the performance of our developed monitors in near real-time measurements of TOC and WSOC, which can be employed for future source apportionment studies in Los Angeles and other areas, aiding in understanding the health impacts of different pollution sources.

While pesticide vapor and particles from agricultural spray drift have been reported to pose a risk to public health, limited baseline ambient measurements exist to warrant an accurate assessment of their impacts at community-to-county-wide scale. Here, we present an initial modeling investigation of the transport and deposition of applied pesticides in an agricultural county in Arizona (Yuma County), to provide initial estimates on the corresponding enhancements in ambient levels of these spray drifts downwind of application sites. With a 50 × 50 km domain, we use the dispersion model CALPUFF with meteorology from the Weather Research and Forecasting (WRF) to investigate the spatiotemporal distribution of pesticide abundance due to spray drift from a representative sample of nine application sites. Data records for nine application days in September and October 2011, which are the peak months of pesticide application, were retroactively simulated for 48-h for all nine application sites using an active ingredient lambda-cyhalothrin, which is a commonly-used pesticide in the county. Twenty-one WRF/CALPUFF simulations were conducted with varying emissions, chemical lifetime, deposition rate, application height, and meteorology inputs, allowing for an ensemble-based analysis on the possible ranges in modeled abundance. Our results show that dispersion of vapors released at time of application heavily depends on prevailing meteorology, particularly wind speed and direction. Dispersion is limited to thin plumes that are easily transported out of the domain. The ensemble-mean vapor concentrations of the 48-h average (> 90 percentile domain-wide) range from 0.2 nanograms (ng)/m to 200 ng/m, and the peak can be as high as 1000 ng/m near the application sites. Pesticide particles are mainly deposited within 1-2 km from the application sites at an average rate of 10 ng/km/h but vary with particle mean diameter and standard deviation. While these findings are generally consistent with reported ambient levels in the literature, the associated ensemble-spread on these estimates are in the same order of magnitude as their ensemble-mean. At the two nearby communities downwind of these sites, we find that peak vapor concentrations are less than 50 ng/m with exposure times of less than an hour, as approximately 99.4% of the vapors are advected out and 99.5% of the particles deposit within the domain. Results of this study indicate pesticide spray drift from a sample of application sites and representative days in Fall may have a limited impact on neighboring communities. However, we strongly suggest that field measurements should be collected for model validation and more rigorous investigation of the actual scale of these impacts when the bulk of pesticide applications across the county, variation in active pesticide ingredients, and potential resuspension of deposited particles are considered.

Numerous studies have used air quality models to estimate pollutant concentrations in the Metropolitan Area of São Paulo (MASP) by using different inputs and assumptions. Our objectives are to summarize these studies, compare their performance, configurations, and inputs, and recommend areas of further research. We examined 29 air quality modeling studies that focused on ozone (O) and fine particulate matter (PM) performed over the MASP, published from 2001 to 2023. The California Institute of Technology airshed model (CIT) was the most used offline model, while the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was the most used online model. Because the main source of air pollution in the MASP is the vehicular fleet, it is commonly used as the only anthropogenic input emissions. Simulation periods were typically the end of winter and during spring, seasons with higher O and PM concentrations. Model performance for hourly ozone is good with half of the studies with Pearson correlation above 0.6 and root mean square error (RMSE) ranging from 7.7 to 27.1 ppb. Fewer studies modeled PM and their performance is not as good as ozone estimates. Lack of information on emission sources, pollutant measurements, and urban meteorology parameters is the main limitation to perform air quality modeling. Nevertheless, researchers have used measurement campaign data to update emission factors, estimate temporal emission profiles, and estimate volatile organic compounds (VOCs) and aerosol speciation. They also tested different emission spatial disaggregation approaches and transitioned to global meteorological reanalysis with a higher spatial resolution. Areas of research to explore are further evaluation of models' physics and chemical configurations, the impact of climate change on air quality, the use of satellite data, data assimilation techniques, and using model results in health impact studies. This work provides an overview of advancements in air quality modeling within the MASP and offers practical approaches for modeling air quality in other South American cities with limited data, particularly those heavily impacted by vehicle emissions.

The spatiotemporal variations of fine particulate matter chemical composition have changed over time in the U.S. and increasing evidence indicated differential toxicity of chemical composition. Thus, comprehensive explanation of -related adverse health impacts in the U.S. necessitated a detailed analysis of spatiotemporal trends of chemical composition. This research aims to analyze the changes in concentrations of and its chemical composition in spatial and temporal scales in the conterminous U.S. The mass concentration and chemical speciation data were downloaded from U.S. EPA Air Quality System (AQS) (2006-2020) to investigate the spatiotemporal changes of and its chemical components. The results indicated that national annual average concentration was significantly reduced from in 2006 to in 2020 with an average reduction of , mainly attributed to inorganic reductions (i.e., ammonium ( ), nitrate , and sulfate ) and the average reductions were , and , respectively. The largest air quality improvements occurred in areas with the worst baseline air quality. Moreover, observed spikes in in California in 2020 were due to higher concentrations of organic matter (OM) and elemental carbon (EC) caused by 2020 wildfires. Furthermore, while levels of , and almost levelled off in recent years, further air quality improvements may require targeting carbonaceous species. The heavily polluted days occurred less frequently in recent years and primary organic carbon (OC) accounted for a larger portion of OC in winter than in summer because of the relatively reduced formation rate of secondary organic aerosol (SOA). Our analysis revealed the spatial and temporal trends of various chemical composition in the conterminous U.S. and provided insights into source contributions, atmospheric chemical conditions, and development of future emissions control strategies.

Brazil experiences significant aerosol loads throughout the year, particularly during the biomass-burning season in the Amazon. Thus, given the importance of aerosols for climate and health, this research aimed to validate and compare Aerosol Optical Depth (AOD) products over Brazil. This evaluation considers three algorithms that retrieve AOD by using data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor: Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC). To validate the satellite data, 17 sunphotometers from the AErosol RObotic NETwork (AERONET) were utilized. The results show a high correlation (R>0.9) between the MODIS-AOD products and ground-based data. However, MODIS-AOD products tend to overestimate or underestimate AOD values, depending on the specific AOD value and algorithm evaluated. Additionally, it was observed that the performance of the algorithms is influenced by factors such as land cover type, view geometry, and the spatiotemporal distribution of aerosols. In particular, challenges were encountered when retrieving robust AOD data for Savanna and Urban cover classes. In conclusion, the results indicate that MAIAC and DB algorithms demonstrate greater stability in retrieving AOD values. Nevertheless, caution should be exercised when applying these products to map aerosols on specific surfaces, such as urban areas.

Epidemiologic studies have consistently observed associations between fine particulate matter (PM) exposure and premature mortality. These studies use air quality concentration information from a combination of sources to estimate pollutant exposures and then assess how mortality varies as a result of differing exposures. Health impact assessments then typically use a single log-linear hazard ratio (HR) per health outcome to estimate counts of avoided human health effects resulting from air quality improvements. This paper estimates the total PM-attributable premature mortality burden using a variety of methods for estimating exposures and quantifying PM-attributable deaths in 2011 and 2028. We use: 1) several exposure models that apply a wide range of methods, and 2) a variety of HRs from the epidemiologic literature that relate long-term PM exposures to mortality among the U.S. population. We then further evaluate the variability of aggregated national premature mortality estimates to stratification by race and/or ethnicity or exposure level (e.g., below the current annual PM National Ambient Air Quality Standards). We find that unstratified annual adult mortality burden incidence estimates vary more (e.g., ~3-fold) by HR than by exposure model (e.g., <10%). In addition, future mortality burden estimates stratified by race/ethnicity are larger than the unstratified estimates of the entire population, and studies that stratify PM-attributable mortality HRs by an exposure concentration threshold led to substantially higher estimates. These results are intended to provide transparency regarding the sensitivity of mortality estimates to upstream input choices.

Under the EU Air Quality Directive (AQD) 2008/50/EC member states are required to undertake routine monitoring of PM composition at background stations. The AQD states for PM speciation this should include at least: nitrate , sulfate , chloride (Cl), ammonium (NH4), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), elemental carbon (EC) and organic carbon (OC). Until 2017, it was the responsibility of each country to determine the methodology used to report the composition for the inorganic components of PM. In August 2017 a European standard method of measurement of PM inorganic chemical components ( , Cl, , Na, K, Mg, Ca) as deposited on filters (EN16913:2017) was published. From August 2019 this then became the European standard method. This filter method is labour-intensive and provides limited time resolution and is prone to losses of volatile compounds. There is therefore increasing interest in the use of alternative automated methods. For example, the UK reports hourly PM chemical composition using the Monitor for AeRosols and Gases in Ambient air (MARGA, Metrohm, NL). This study is a pre-assessment review of available data to demonstrate if or to what extent equivalence is possible using either the MARGA or other available automatic methods, including the Aerosol Chemical Speciation Monitor (ACSM, Aerodyne Research Inc. US) and the Ambient Ion Monitor (AIM, URG, US). To demonstrate equivalence three objectives were to be met. The first two objectives focused on data capture and were met by all three instruments. The third objective was to have less than a 50% expanded uncertainty compared to the reference method for each species. Analysis of this objective was carried out using existing paired datasets available from different regions around the world. It was found that the MARGA (2006-2019 model) had the potential to demonstrate equivalence for all species in the standard, though it was only through a combination of case studies that it passed uncertainty criteria. The ACSM has the potential to demonstrate equivalence for , and in some conditions , but did not for Cl due to its inability to quantify refractory aerosol such as sea salt. The AIM has the potential for , , , Cl and Mg. Future investigations are required to determine if the AIM could be optimised to meet the expanded uncertainty criterion for Na, K and Ca. The recommendation is that a second stage to demonstrate equivalence is required which would include both laboratory and field studies of the three candidate methods and any other technologies identified with the potential to report the required species.

Random Forest algorithms have extensively been used to estimate ambient air pollutant concentrations. However, the accuracy of model-predicted estimates can suffer from extrapolation problems associated with limited measurement data to train the machine learning algorithms. In this study, we developed and evaluated two approaches, incorporating low-cost sensor data, that enhanced the extrapolating ability of random-forest models in areas with sparse monitoring data. Rochester, NY is the area of a pregnancy-cohort study. Daily PM concentrations from the NAMS/SLAMS sites were obtained and used as the response variable in the model, with satellite data, meteorological, and land-use variables included as predictors. To improve the base random-forest models, we used PM measurements from a pre-existing low-cost sensors network, and then conducted a two-step backward selection to gradually eliminate variables with potential emission heterogeneity from the base models. We then introduced the regression-enhanced random forest method into the model development. Finally, contemporaneous urinary 1-hydroxypyrene was used to evaluate the PM predictions generated from the two approaches. The two-step approach increased the average external validation R from 0.49 to 0.65, and decreased the RMSE from 3.56 μg/m to 2.96 μg/m. For the regression-enhanced random forest models, the average R of the external validation was 0.54, and the RMSE was 3.40 μg/m. We also observed significant and comparable relationships between urinary 1-hydroxypyrene levels and PM predictions from both improved models. This PM model estimation strategy could improve the extrapolating ability of random forest models in areas with sparse monitoring data.

Low-cost air quality monitors are growing in popularity among both researchers and community members to understand variability in pollutant concentrations. Several studies have produced calibration approaches for these sensors for ambient air. These calibrations have been shown to depend primarily on relative humidity, particle size distribution, and particle composition, which may be different in indoor environments. However, despite the fact that most people spend the majority of their time indoors, little is known about the accuracy of commonly used devices indoors. This stems from the fact that calibration data for sensors operating in indoor environments are rare. In this study, we sought to evaluate the accuracy of the raw data from PurpleAir fine particulate matter monitors and for published calibration approaches that vary in complexity, ranging from simply applying linear corrections to those requiring co-locating a filter sample for correction with a gravimetric concentration during a baseline visit. Our data includes PurpleAir devices that were co-located in each home with a gravimetric sample for 1-week periods (265 samples from 151 homes). Weekly-averaged gravimetric concentrations ranged between the limit of detection (3 μg/m) and 330 μg/m. We found a strong correlation between the PurpleAir monitor and the gravimetric concentration (R>0.91) using internal calibrations provided by the manufacturer. However, the PurpleAir data substantially overestimated indoor concentrations compared to the gravimetric concentration (mean bias error ≥ 23.6 μg/m using internal calibrations provided by the manufacturer). Calibrations based on ambient air data maintained high correlations (R ≥ 0.92) and substantially reduced bias (e.g. mean bias error = 10.1 μg/m using a US-wide calibration approach). Using a gravimetric sample from a baseline visit to calibrate data for later visits led to an improvement over the internal calibrations, but performed worse than the simpler calibration approaches based on ambient air pollution data. Furthermore, calibrations based on ambient air pollution data performed best when weekly-averaged concentrations did not exceed 30 μg/m, likely because the majority of the data used to train these models were below this concentration.

We analyze hourly PM (particles with an aerodynamic diameter of ≤ 2.5 μm) concentrations measured at the U.S. Embassy in Dhaka over the 2016 - 2021 time period and find that concentrations are seasonally dependent with the highest occurring in winter and the lowest in monsoon seasons. Mean winter PM concentrations reached ~165-175 μg/m while monsoon concentrations remained ~30-35 μg/m. Annual mean PM concentration reached ~5-6 times greater than the Bangladesh annual PM standard of 15 μg/m. The number of days exceeding the daily PM standard of 65 μg/m in a year approached nearly 50%. Daily-mean PM concentrations remained elevated (>65 μg/m) for more than 80 consecutive days. Night-time concentrations were greater than daytime concentrations. The comparison of results obtained from the Community Multiscale Air Quality (CMAQ) model simulations over the Northern Hemisphere using 108-km horizontal grids with observed data suggests that the model can reproduce the seasonal variation of observed data but underpredicts observed PM in winter months with a normalized mean bias of 13-32%. In the model, organic aerosol is the largest component of PM, of which secondary organic aerosol plays a dominant role. Transboundary pollution has a large impact on the PM concentration in Dhaka, with an annual mean contribution of ~40 μg/m.