The reduction of summer sulfate and switch from summertime to wintertime PM2.5 concentration maxima in the United States

Enhancement of atmospheric oxidation capacity induced co-pollution of the O3 and PM2.5 in Lanzhou, northwest China

In recent years, the co-pollution of surface ozone (O3) and fine particulate matter (PM2.5) has emerged as a critical concern within specific regions of China's atmospheric environment. This study employed a comprehensive approach by integrating statistical analysis with the interpretable ensemble machine learning model. Delving deeply into the intricate mechanisms behind O3 and PM2.5 co-pollution in Lanzhou city from 2019 to 2022, the research synthesized and analyzed an array of data sources, including ground observations, a multi-parameter lidar system, and meteorological data. Our findings, derived from ground observations to vertical distribution, unequivocally confirm that the enhancement of atmospheric oxidation capacity serves as a critical driver in the genesis of secondary particles, playing a substantial role in the augmented levels of O3 and PM2.5 experienced during the warm season. Moreover, the impact of local weather patterns is indispensable as it precipitates a relatively stable mid-level atmosphere, culminating in elevated surface concentrations of both PM2.5 and O3. Overall, this study emphatically underscores the importance of adopting a comprehensive approach to address these environmental challenges.

In situ biomass burning enhanced the contribution of biogenic sources to sulfate aerosol in subtropical cities

Sulfurous gases released by biogenic sources play a key role in the global sulfur cycle. However, the contribution of biogenic sources to sulfate aerosol in the urban atmosphere has received little attention. Emission sources and formation process of sulfate in Guangzhou, a subtropical mega-city in China, were clarified using multiple methods, including isotope tracers and chemical markers. The δ18O of sulfate suggested that secondary sulfate was the dominant component (84 %) of sulfate aerosol, which mainly formed by transition metal ion (TMI) catalyzed oxidation (31 %) and OH radical oxidation (30 %). The factors driving secondary sulfate formation were revealed using a tree boosting model, which suggested that NH3, temperature, and oxidants were the most important factors. The δ34S of sulfate indicated that biogenic sources accounted for annual average of 26.0 % of the sulfate, which increased to 30.4 % in winter monsoon period. Rice straw burning enhanced sulfate formation by promoting the release of reduced sulfur from soil, which is rapidly converted into sulfate under a subtropical urban atmosphere with high concentration of NH3 and oxidants. This study revealed the important influence of rice straw burning on biogenic sulfur emission during the rice harvest, thereby providing insight into the sulfur cycle and regional air pollution.

Evaluating reduced-form modeling tools for simulating ozone and PM2.5 monetized health impacts

Reduced-form modeling approaches are an increasingly popular way to rapidly estimate air quality and human health impacts related to changes in air pollutant emissions. These approaches reduce computation time by making simplifying assumptions about pollutant source characteristics, transport and chemistry. Two reduced form tools used by the Environmental Protection Agency in recent assessments are source apportionment-based benefit per ton (SA BPT) and source apportionment-based air quality surfaces (SABAQS). In this work, we apply these two reduced form tools to predict changes in ambient summer-season ozone, ambient annual PM2.5 component species and monetized health benefits for multiple sector-specific emission control scenarios: on-road mobile, electricity generating units (EGUs), cement kilns, petroleum refineries, and pulp and paper facilities. We then compare results against photochemical grid and standard health model-based estimates. We additionally compare monetized PM2.5 health benefits to values derived from three reduced form tools available in the literature: the Intervention Model for Air Pollution (InMAP), Air Pollution Emission Experiments and Policy Analysis (APEEP) version 2 (AP2) and Estimating Air pollution Social Impact Using Regression (EASIUR). Ozone and PM2.5 changes derived from SABAQS for EGU scenarios were well-correlated with values obtained from photochemical modeling simulations with spatial correlation coefficients between 0.64 and 0.89 for ozone and between 0.75 and 0.94 for PM2.5. SABAQS ambient ozone and PM2.5 bias when compared to photochemical modeling predictions varied by emissions scenario: SABAQS PM2.5 changes were overpredicted by up to 46% in one scenario and underpredicted by up to 19% in another scenario; SABAQS seasonal ozone changes were overpredicted by 34% to 83%. All tools predicted total PM2.5 benefits within a factor of 2 of the full-form predictions consistent with intercomparisons of reduced form tools available in the literature. As reduced form tools evolve, it is important to continue periodic comparison with comprehensive models to identify systematic biases in estimating air pollution impacts and resulting monetized health benefits.

Leveraging Citizen Science and Low-Cost Sensors to Characterize Air Pollution Exposure of Disadvantaged Communities in Southern California

Assessing exposure to fine particulate matter (PM_2.5) across disadvantaged communities is understudied, and the air monitoring network is inadequate. We leveraged emerging low-cost sensors (PurpleAir) and engaged community residents to develop a community-based monitoring program across disadvantaged communities (high proportions of low-income and minority populations) in Southern California. We recruited 22 households from 8 communities to measure residential outdoor PM_2.5 concentrations from June 2021 to December 2021. We identified the spatial and temporal patterns of PM_2.5 measurements as well as the relationship between the total PM_2.5 measurements and diesel PM emissions. We found that communities with a higher percentage of Hispanic and African American population and higher rates of unemployment, poverty, and housing burden were exposed to higher PM_2.5 concentrations. The average PM_2.5 concentrations in winter (25.8 µg/m³) were much higher compared with the summer concentrations (12.4 µg/m³). We also identified valuable hour-of-day and day-of-week patterns among disadvantaged communities. Our results suggest that the built environment can be targeted to reduce the exposure disparity. Integrating low-cost sensors into a citizen-science-based air monitoring program has promising applications to resolve monitoring disparity and capture “hotspots” to inform emission control and urban planning policies, thus improving exposure assessment and promoting environmental justice.

The Effect of Transportation and Wildfires on the Spatiotemporal Heterogeneity of PM2.5 Mass in the New York-New Jersey Metropolitan Statistical Area

Declining ambient PM_2.5 concentrations have been attributed to fuel consumption standards and emission controls of secondary sulfate and nitrate aerosol precursors from transportation and industrial sectors. As a result, the relative contribution of PM_2.5 sources is modified, shifting PM_2.5 trends, physicochemical characteristics, and health effects. Carbonaceous fine aerosol account for most of PM_2.5 mass in the US. This study aims to examine the spatiotemporal trends of ambient PM_2.5 levels and their association with primary PM_2.5 emissions from anthropogenic activities and fires in the New York/New Jersey metropolitan statistical area (NYNJ MSA) airshed. PM_2.5 mass concentrations were obtained from the U.S. Environmental Protection Agency (USEPA) Air Data. Ambient PM_2.5 mass levels declined on average by 47%, at a rate of -0.61 ± 0.01 μg/m³/year in urban locations and -0.25 ± 0.01 μg/m³/year in upwind and peri-urban locations over the 2007 to 2017 period. The strong spatial gradient in 2007, with high PM_2.5 levels in urban locations and low PM_2.5 levels in peri-urban locations gradually weakened by 2013 but re-appeared in 2017. Over the same period, primary PM_2.5 emissions declined by 52% from transportation, 15% from industrial, and 8% from other anthropogenic sources corresponding to a decrease of 0.8, 0.9, and 0.6 μg/m³ on ambient PM_2.5 mass, respectively. Wildland and prescribed fires emissions increased more than 3 times adding 0.8 μg/m³ to ambient PM_2.5 mass. These results indicate that (i) fire emissions may impede the effectiveness of existing policies to improve air quality and (ii) the chemical content of PM_2.5 may be changing to an evolving mixture of aromatic and oxygenated organic species with differential toxicological responses as compared to inert ammonium sulfate and nitrate salts.

Utah Wintertime Measurements of Heavy-Duty Vehicle Nitrogen Oxide Emission Factors

There have only been a few wintertime studies of heavy-duty vehicle (HDV) NO_x emissions in the United States, and while they have observed increased emissions, fleet characterization to identify the cause has been lacking. We have collected wintertime measurements of NO_x emission factors from 1591 HDVs at a Utah Port of Entry in December 2020 that includes individual vehicle identification. In general, NO_x emission factors for 2011 and newer chassis model year HDV are significantly higher than those for 2017 spring measurements from California. The newest chassis model year HDV (2017-2021) NO_x emission factors are similar, indicating no significant emission deterioration over the 5 year period, though they are still approximately a factor of 3 higher than the portable emission measurement on-road enforcement standard. We estimate that ambient temperature increases NO_x emissions no more than 25% in the newer HDV, likely through reductions in catalyst efficiencies. NO_x emissions increase to a significantly higher level for the 2011-2013 chassis model year vehicles, where within the uncertainties, they have emissions similar to older precontrol vehicles, indicating that they have lost their NO_x control capabilities within 8 years. MOVES3 modeling of the Utah fleet underpredicted mean NO_x emissions by a factor of 1.8 but the MOVES3 estimate is helped by including a larger fraction of high-emitting glider kit trucks (new chassis with pre-emission control engines) than found in the observations.

Revisiting Total Particle Number Measurements for Vehicle Exhaust Regulations

Road transport significantly contributes to air pollution in cities. Emission regulations have led to significantly reduced emissions in modern vehicles. Particle emissions are controlled by a particulate matter (PM) mass and a solid particle number (SPN) limit. There are concerns that the SPN limit does not effectively control all relevant particulate species and there are instances of semi-volatile particle emissions that are order of magnitudes higher than the SPN emission levels. This overview discusses whether a new metric (total particles, i.e., solids and volatiles) should be introduced for the effective regulation of vehicle emissions. Initially, it summarizes recent findings on the contribution of road transport to particle number concentration levels in cities. Then, both solid and total particle emission levels from modern vehicles are presented and the adverse health effects of solid and volatile particles are briefly discussed. Finally, the open issues regarding an appropriate methodology (sampling and instrumentation) in order to achieve representative and reproducible results are summarized. The main finding of this overview is that, even though total particle sampling and quantification is feasible, details for its realization in a regulatory context are lacking. It is important to define the methodology details (sampling and dilution, measurement instrumentation, relevant sizes, etc.) and conduct inter-laboratory exercises to determine the reproducibility of a proposed method. It is also necessary to monitor the vehicle emissions according to the new method to understand current and possible future levels. With better understanding of the instances of formation of nucleation mode particles it will be possible to identify its culprits (e.g., fuel, lubricant, combustion, or aftertreatment operation). Then the appropriate solutions can be enforced and the right decisions can be taken on the need for new regulatory initiatives, for example the addition of total particles in the tailpipe, decrease of specific organic precursors, better control of inorganic precursors (e.g., NH3, SOx), or revision of fuel and lubricant specifications.

Secondary organic aerosol association with cardiorespiratory disease mortality in the United States

Fine particle pollution, PM2.5, is associated with increased risk of death from cardiorespiratory diseases. A multidecadal shift in the United States (U.S.) PM2.5 composition towards organic aerosol as well as advances in predictive algorithms for secondary organic aerosol (SOA) allows for novel examinations of the role of PM2.5 components on mortality. Here we show SOA is strongly associated with county-level cardiorespiratory death rates in the U.S. independent of the total PM2.5 mass association with the largest associations located in the southeastern U.S. Compared to PM2.5, county-level variability in SOA across the U.S. is associated with 3.5× greater per capita county-level cardiorespiratory mortality. On a per mass basis, SOA is associated with a 6.5× higher rate of mortality than PM2.5, and biogenic and anthropogenic carbon sources both play a role in the overall SOA association with mortality. Our results suggest reducing the health impacts of PM2.5 requires consideration of SOA.

Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.

Influence of Krakow Winter and Summer Dusts on the Redox Cycling of Vitamin B12a in the Presence of Ascorbic Acid

Air pollution remains a serious problem in Krakow, Poland. According to the European Environmental Agency, annual mean levels of both PM2.5 and PM10 recorded in Krakow are much higher than EU limit values. Thus, the influence of particulate matter (PM) on the function of living organisms, as well as different physiological processes, is an urgent subject to be studied. The reported research forms part of the multi-disciplinary project ‘Air Pollution versus Autoimmunity: Role of multiphase aqueous Inorganic Chemistry,’ which aims to demonstrate the PM effect on the immune system. The present studies focused on the role of dust collected in Krakow on the redox cycling of vitamin B12a in the presence of ascorbic acid. Dust samples collected during the winter 2019/2020 and summer 2020 months in the city center of Krakow were characterized using various analytical techniques. The influence of Krakow dusts on the kinetics of the reaction between nitrocobalamin and ascorbic acid was confirmed and discussed in terms of the composition of the samples. Possible reasons for the reported findings are provided.

Coupled Air Quality and Boundary-Layer Meteorology in Western U.S. Basins during Winter: Design and Rationale for a Comprehensive Study

Wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical-meteorological interactions that drive high pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in Western U.S. basins. Approximately 120 people participated, representing 50 institutions and 5 countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary-layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological-chemical linkages outlined here, nor to validate complex processes within coupled atmosphere-chemistry models.

Spatiotemporal trends of PM2.5 and its major chemical components at urban sites in Canada

To evaluate the effectiveness of emission control regulations designed for reducing air pollution, chemically resolved PM_2.5 data have been collected across Canada through the National Air Pollution Surveillance network in the past decade. 24-hr time integrated PM_2.5 collected at seven urban and two rural sites during 2010-2016 were analyzed to characterize geographical and seasonal patterns and associated potential causes. Site-specific seven-year mean gravimetric PM_2.5 mass concentrations ranged from 5.7 to 9.6 µg/m³. Seven-year mean concentrations of SO₄^2-, NO₃^-, NH₄⁺, organic carbon (OC), and elemental carbon (EC) were in the range of 0.68 to 1.6, 0.21 to 1.5, 0.27 to 0.71, 1.1 to 1.9, and 0.37 to 0.71 µg /m³, accounting for 10.8%-18.1%, 3.7%-16.7%, 4.7%-7.4%, 18.4%-21.0%, and 6.4%-10.6%, respectively, of gravimetric PM_2.5 mass. PM_2.5 and its five major chemical components showed higher concentrations in southeastern Canada and lower values in Atlantic Canada, with the seven-year mean ratios between the two regions being on the order of 1.7 for PM_2.5 and 1.8-7.1 for its chemical components. When comparing the concentrations between urban and rural sites within the same region, those of SO₄^2- and NH₄⁺ were comparable, while those of NO₃^-, OC, and EC were around 20%, 40%-50%, and 70%-80%, respectively, higher at urban than rural sites, indicating the regional scale impacts of SO₄^2- and NH₄⁺ and effects of local sources on OC and EC. Monthly variations generally showed summertime peaks for SO₄^2- and wintertime peaks for NO₃^-, but those of NH₄⁺, OC, and EC exhibited different seasonality at different locations.

Impact of dimethylsulfide chemistry on air quality over the Northern Hemisphere

We implement oceanic dimethylsulfide (DMS) emissions and its atmospheric chemical reactions into the Community Multiscale Air Quality (CMAQv53) model and perform annual simulations without and with DMS chemistry to quantify its impact on tropospheric composition and air quality over the Northern Hemisphere. DMS chemistry enhances both sulfur dioxide (SO2) and sulfate (S O 4 2 - ) over seawater and coastal areas. It enhances annual mean surface SO2 concentration by +46 pptv andS O 4 2 - by +0.33 μg/m3 and decreases aerosol nitrate concentration by -0.07 μg/m3 over seawater compared to the simulation without DMS chemistry. The changes decrease with altitude and are limited to the lower atmosphere. Impacts of DMS chemistry onS O 4 2 - are largest in the summer and lowest in the fall due to the seasonality of DMS emissions, atmospheric photochemistry and resultant oxidant levels. Hydroxyl and nitrate radical-initiated pathways oxidize 75% of the DMS while halogen-initiated pathways oxidize 25%. DMS chemistry leads to more acidic particles over seawater by decreasing aerosol pH. IncreasedS O 4 2 - from DMS enhances atmospheric extinction while lower aerosol nitrate reduces the extinction so that the net effect of DMS chemistry on visibility tends to remain unchanged over most of the seawater.

Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM2.5) using satellite data over large regions

Reconstructing the distribution of fine particulate matter (PM_2.5) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM_2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM_2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM_2.5 on a 1×1 km² resolution for a 13 state region in the Northeastern USA for the years 2000-2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m³ in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m³ using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM_2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

Chemical Compositions and Sources Contribution of Atmospheric Particles at a Typical Steel Industrial Urban Site

Online monitoring concentrations of PM at five sites were obtained from 01/01/2016 to 31/12/2016 in Laiwu, China, and PM2.5 filters were manually sampled for total 34 days at the same sites in four seasons in 2016. PM pollution sources, including soil dust, urban dust, construction dust, coal-fired power plants dust, steel plant dust and motor vehicle exhaust dust were sampled, respectively. The overall mean PM2.5/PM10 ratio (0.57) in Laiwu was at a relatively lower level compared with that in other Chinese cities, which was higher in winter, indicating fine particulate was the main contributor of atmospheric pollution in this period. NH4+ mainly existed in the form of NH4NO3 and (NH4)2SO4 during the sampling periods. Higher sulfate and NH4+ concentrations were in summer while higher nitrate concentrations prevailed in winter. The annual value of OC/EC was (5.38 ± 1.70), higher in summer and lower in winter, and the calculated SOC/OC value (%) was (43.68 ± 12.98)%. The characteristic components were Si, Fe and Ca in urban dust and soil dust; Ca, Mg, and NH4+ in construction dust; Fe, Ca and SO42- in steel dust; OC, EC and Si in motor vehicle exhaust dust; SO42-, Al and NH4+ in power plant dust. Compared with other cities at home and abroad, it was found that the concentrations of metal elements in Laiwu were significantly higher than those in foreign cities, and at a medium level in China. With the improved CRAESCMB model, the urban dust was regarded as the receptor and the source of PM2.5 and apportioned its secondary sources contributions to PM2.5. The CMB results showed the contributions of secondary sources including sulfate (17%), nitrate (17%) and SOC (13%) to PM2.5 accounted for nearly half of all sources. Therefore, more attentions should be paid on secondary sources from the primary emission sources of the motor vehicle exhaust, coal combustion sources especially.

An overview of selected emerging outdoor airborne pollutants and air quality issues: The need to reduce uncertainty about environmental and human impacts

According to the literature, it is estimated that outdoor air pollution is responsible for the premature death in a range from 3.7 to 8.9 million persons on an annual basis across the world. Although there is uncertainty on this figure, outdoor air pollution represents one of the greatest global risks to human health. In North America, the rapid evolution of technologies (e.g., nanotechnology, unconventional oil and gas rapid development, higher demand for fertilizers in agriculture) and growing demand for ground, marine and air transportation may result in significant increases of emissions of pollutants that have not been carefully studied so far. As a result, these atmospheric pollutants insufficiently addressed by science in Canada and elsewhere are becoming a growing issue with likely human and environmental impacts in the near future. Here, an emerging pollutant is defined as one that meets the following criteria: 1) potential or demonstrated risk for humans or the environment, 2) absence of Canada-wide national standard, 3) insufficient routine monitoring, 4) yearly emissions greater than one ton in Canada, 5) insufficient data concerning significant sources, fate, and detection limit, and 6) insufficiently addressed by epidemiological studies. A new methodology to rank emerging pollutants is proposed here based on weighting multiple criteria. Some selected emerging issues are also discussed here and include the growing concern of ultrafine or nanoparticles, growing ammonia emissions (due to rapid expansion of the agriculture), increased methane/ethane/propane emissions (due to the expanding hydraulic fracturing in the oil and gas sector) and the growing transportation sector. Finally, the interaction between biological and anthropogenic pollution has been found to be a double threat for public health. Here, a multidisciplinary and critical overview of selected emerging pollutants and related critical issues is presented with a focus in Canada.Implications: This overview paper provides a selection methodology for emerging pollutants in the atmospheric environment. It also provides a critical discussion of some related issues. The ultimate objective is to inform about the need to 1) address emerging issues through adequate surface monitoring and modeling in order to inform the development of regulations, 2) reduce uncertainties by geographically mapping emerging pollutants (e.g., through data fusion, data assimilation of observations into air quality models) which can improve the scientific support of epidemiological studies and policies. This review also highlights some of the difficulties with the management of these emerging pollutants, and the need for an integrated approach.

Incorporation of Remote PM2.5 Concentrations into the Downscaler Model for Spatially Fused Air Quality Surfaces

The United States Environmental Protection Agency (EPA) has implemented a Bayesian spatial data fusion model called the Downscaler (DS) model to generate daily air quality surfaces for PM2.5 across the contiguous U.S. Previous implementations of DS relied on monitoring data from EPA's Air Quality System (AQS) network, which is largely concentrated in urban areas. In this work, we introduce to the DS modeling framework an additional PM2.5 input dataset from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network located mainly in remote sites. In the western U.S. where IMPROVE sites are relatively dense (compared to the eastern U.S.), the inclusion of IMPROVE PM2.5 data to the DS model runs reduces predicted annual averages and 98th percentile concentrations by as much as 1.0 and 4 μg m-3, respectively. Some urban areas in the western U.S., such as Denver, Colorado, had moderate increases in the predicted annual average concentrations, which led to a sharpening of the gradient between urban and remote areas. Comparison of observed and DS-predicted concentrations for the grid cells containing IMPROVE and AQS sites revealed consistent improvement at the IMPROVE sites but some degradation at the AQS sites. Cross-validation results of common site-days withheld in both simulations show a slight reduction in the mean bias but a slight increase in the mean square error when the IMPROVE data is included. These results indicate that the output of the DS model (and presumably other Bayesian data fusion models) is sensitive to the addition of geographically distinct input data, and that the application of such models should consider the prediction domain (national or urban focused) when deciding to include new input data.

Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors

An accurate fine-resolution surface of the chemical composition of fine particulate matter (PM_2.5) would offer valuable information for epidemiological studies and health impact assessments. We develop geoscience-derived estimates of PM_2.5 composition from a chemical transport model (GEOS-Chem) and satellite observations of aerosol optical depth, and statistically fuse these estimates with ground-based observations using a geographically weighted regression over North America to produce a spatially complete representation of sulfate, nitrate, ammonium, black carbon, organic matter, mineral dust, and sea-salt over 2000-2016. Significant long-term agreement is found with cross-validation sites over North America (R² = 0.57-0.96), with the strongest agreement for sulfate (R² = 0.96), nitrate (R² = 0.90), and ammonium (R² = 0.86). We find that North American decreases in population-weighted fine particulate matter (PM_2.5) concentrations since 2000 have been most heavily influenced by regional changes in sulfate and organic matter. Regionally, the relative importance of several chemical components are found to change with PM_2.5 concentration, such as higher PM_2.5 concentrations having a larger proportion of nitrate and a smaller proportion of sulfate. This data set offers information for research into the health effects of PM_2.5 chemical components.

The Association between Respiratory Infection and Air Pollution in the Setting of Air Quality Policy and Economic Change

RATIONALE

Fine particulate matter air pollution of 2.5 μm or less in diameter (PM2.5) has been associated with an increased risk of respiratory disease, but assessments of specific respiratory infections in adults are lacking.

OBJECTIVES

To estimate the rate of respiratory infection healthcare encounters in adults associated with acute increases in PM2.5 concentrations.

METHODS

Using case-crossover methods, we studied 498,118 adult New York State residents with a primary diagnosis of influenza, bacterial pneumonia, or culture-negative pneumonia upon hospitalization or emergency department (ED) visit (2005-2016). We estimated the relative rate of healthcare encounters associated with increases in PM2.5 in the previous 1-7 days and explored differences before (2005-2007), during (2008-2013), and after (2014-2016) implementation of air quality policies and economic changes.

RESULTS

Interquartile range increases in PM2.5 over the previous 7 days were associated with increased excess rates (ERs) of culture-negative pneumonia hospitalizations (2.5%; 95% confidence interval [CI], 1.7-3.2%) and ED visits (2.5%; 95% CI, 1.4-3.6%), and increased ERs of influenza ED visits (3.9%; 95% CI, 2.1-5.6%). Bacterial pneumonia hospitalizations, but not ED visits, were associated with increases in PM2.5 and, though imprecise, were of a similar magnitude to culture-negative pneumonia (Lag Day 6 ER, 2.3%; 95% CI, 0.3-4.3). Increased relative rates of influenza ED visits and culture-negative pneumonia hospitalizations were generally larger in the "after" period (P < 0.025 for both outcomes), compared with the "during" period, despite reductions in overall PM2.5 concentrations.

CONCLUSIONS

Increased rates of culture-negative pneumonia and influenza were associated with increased PM2.5 concentrations during the previous week, which persisted despite reductions in PM2.5 from air quality policies and economic changes. Though unexplained, this temporal variation may reflect altered toxicity of different PM2.5 mixtures or increased pathogen virulence.

The effect of natural and anthropogenic factors on PM2.5: Empirical evidence from Chinese cities with different income levels

The aim of this paper is to estimate the effects of natural conditions and anthropogenic factors on PM_2.5 concentrations, taking into consideration differences in the income levels, and thus the development stages, of the cities studied. To achieve this goal, a balanced dataset of 287 Chinese cities was divided into different income-based panels for the period 1998-2015. The empirical estimation results indicated that meteorological conditions exerted varied effects on PM_2.5 concentrations across different income-based panels. The results show that the coefficients of temperature were positive and significant in all panels, with the exception of upper-middle-income cities. Whilst wind speed and precipitation were found to be conducive to reducing PM_2.5 concentrations, no such significant correlation was found in relation to relative humidity (except in high-income cities). In terms of the anthropogenic factors addressed in the study, we found an inverted U-shaped relationship between economic development and PM_2.5 concentrations, confirming the Environmental Kuznets Curve hypothesis. In addition, the industrial structure and road density were observed to exert significant positive impacts on PM_2.5 concentrations. The empirical analysis of the effects of FDI on PM_2.5 concentrations indicate that FDI aggravated PM_2.5 pollutions in the total cities and lower-middle-income cities panels, supporting the Pollution Haven Hypothesis. The empirical results for population density suggested that it does not significantly influence PM_2.5 concentrations. Moreover, we found that built-up area exerts mixed effects on PM_2.5 concentrations. These results cast a new light on the issue of PM_2.5 pollution for government policy makers tasked with formulating measures to mitigate the concentration of such pollutants, encouraging that consideration be given to the differences between cities with different income levels.

Sources of pollution and interrelationships between aerosol and precipitation chemistry at a central California site

This study examines co-located aerosol and precipitation chemistry data between 2010 and 2016 at Pinnacles National Monument ~65 km east of the coastline in central California. Positive matrix factorization analysis of the aerosol composition data revealed seven distinct pollutant sources: aged sea salt (25.7% of PM2.5), biomass burning (24.2% of PM2.5), fresh sea salt (15.0% of PM2.5), secondary sulfate (11.7% of PM2.5), dust (10.0% of PM2.5), vehicle emissions (8.2% of PM2.5), and secondary nitrate (5.2% of PM2.5). The influence of meteorology and transport on monthly patterns of PM2.5 composition is discussed. Only secondary sulfate exhibited a statistically significant change (a reduction) over time among the PM2.5 source factors. In contrast, PMcoarse exhibited a significant increase most likely due to dust influence. Monthly profiles of precipitation chemistry are summarized showing that the most abundant species in each month was either SO42-, NO3-, or Cl-. Intercomparisons between the precipitation and aerosol data revealed several features: (i) precipitation pH was inversely related to factors associated with more acidic aerosol constituents such as secondary sulfate and aged sea salt, in addition to being reduced by uptake of HNO3 in the liquid phase; (ii) two aerosol source factors (dust and aged sea salt) and PMcoarse exhibited a positive association with Ca2+ in precipitation, suggestive of directly emitted aerosol types with larger sizes promoting precipitation; and (iii) sulfate levels in both the aerosol and precipitation samples analyzed were significantly correlated with dust and aged sea salt PMF factors, pointing to the partitioning of secondary sulfate to dust and sea salt particles. The results of this work have implications for the region's air quality and hydrological cycle, in addition to demonstrating that the use of co-located aerosol and precipitation chemistry data can provide insights relevant to aerosol-precipitation interactions.

Assessing PM2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015

Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007-2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007-2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.