Source apportionment of primary and secondary PM2.5: Associations with pediatric respiratory disease emergency department visits in the U.S. State of Georgia

Sources of acellular oxidative potential of water-soluble fine ambient particulate matter in the midwestern United States

Ambient fine particulate matter (PM2.5) is associated with numerous health complications, yet the specific PM2.5 chemical components and their emission sources contributing to these health outcomes are understudied. Our study analyzes the chemical composition of PM2.5 collected from five distinct locations at urban, roadside and rural environments in midwestern region of the United States, and associates them with five acellular oxidative potential (OP) endpoints of water-soluble PM2.5. Redox-active metals (i.e., Cu, Fe, and Mn) and carbonaceous species were correlated with most OP endpoints, suggesting their significant role in OP. We conducted a source apportionment analysis using positive matrix factorization (PMF) and found a strong disparity in the contribution of various emission sources to PM2.5 mass vs. OP. Regional secondary sources and combustion-related aerosols contributed significantly (> 75 % in total) to PM2.5 mass, but showed weaker contribution (43-69 %) to OP. Local sources such as parking emissions, industrial emissions, and agricultural activities, though accounting marginally to PM2.5 mass (< 10 % for each), significantly contributed to various OP endpoints (10-50 %). Our results demonstrate that the sources contributing to PM2.5 mass and health effects are not necessarily same, emphasizing the need for an improved air quality management strategy utilizing more health-relevant PM2.5 indicators.

PM2.5 and its components and respiratory disease healthcare encounters - Unanticipated increased exposure-response relationships in recent years after environmental policies

Prior studies reported excess rates (ERs) of cardiorespiratory events associated with short-term increases in PM2.5 concentrations, despite implementation of pollution-control policies. In 2017, Federal Tier 3 light-duty vehicle regulations began, and to-date there have been no assessments of population health effects of the policy. Using the NYS Statewide Planning and Research Cooperative System (SPARCS) database, we obtained hospitalizations and ED visits with a principal diagnosis of asthma or chronic obstructive pulmonary disease (COPD) for residents living within 15 miles of six urban PM2.5 monitoring sites in NYS (2014-2019). We used a time-stratified case-crossover design and conditional logistic regression (adjusting for ambient temperature, relative humidity, and weekday) to estimate associations between PM2.5, POC (primary organic carbon), SOC (secondary organic carbon), and rates of respiratory disease hospitalizations and emergency department (ED) visits from 2014 to 2019. We evaluated demographic disparities in these relative rates and compared changes in ERs before (2014-2016) and after Tier 3 implementation (2017-2019). Each interquartile range increase in PM2.5 was associated with increased ERs of asthma or COPD hospitalizations and ED visits in the previous 7 days (ERs ranged from 1.1%-3.1%). Interquartile range increases in POC were associated with increased rates of asthma ED visits (lag days 0-6: ER = 2.1%, 95% CI = 0.7%, 3.6%). Unexpectedly, the ERs of asthma admission and ED visits associated with PM2.5, POC, and SOC were higher during 2017-2019 (after Tier 3) than 2014-2016 (before Tier-3). Chronic obstructive pulmonary disease analyses showed similar patterns. Excess Rates were higher in children (<18 years; asthma) and seniors (≥65 years; COPD), and Black, Hispanic, and NYC residents. In summary, unanticipated increases in asthma and COPD ERs after Tier-3 implementation were observed, and demographic disparities in asthma/COPD and PM2.5, POC, and SOC associations were also observed. Future work should confirm findings and investigate triggering of respiratory events by source-specific PM.

Environmental determinants of health: Measuring multiple physical environmental exposures at the United States census tract level

Physical environment plays a key role in determining human health risks. Exposure to toxins, weather extremes, degraded air and water quality, high levels of noise and limited accessibility to green areas can negatively affect health. Furthermore, adverse environmental exposures are often correlated with each other and with socioeconomic status, thereby compounding disadvantages in marginalized populations. Moreover, despite their importance in determining human health risks, the role of multiple environmental exposures is not well studied, and only a few resources contain aggregate environmental exposure data and only for selected areas of the contiguous US. To fill these gaps, we took a cumulative approach to measuring the environment by generating a composite Multi-Exposure Environmental Index (MEEI) as a US Census Tract-level summary of key environmental factors with known health effects. This measure quantifies multiple environmental exposures in the same area that can result in additive and synergistic effects on health outcomes. This information is crucial to better understand and possibly leverage environmental determinants of health for informed policy-making and intervention.

High spatial-resolved source-specific exposure and risk in the city scale: Influence of spatial interrelationship between PM2.5 sources and population on exposure

Research on High Spatial-Resolved Source-Specific Exposure and Risk (HSRSSER) was conducted based on multiple-year, multiple-site synchronous measurement of PM2.5-bound (particulate matter with aerodynamic diameter<2.5 μm) toxic components in a Chinese megacity. The developed HSRSSER model combined the Positive Matrix Factorization (PMF) and Land Use Regression (LUR) to predict high spatial-resolved source contributions, and estimated the source-specific exposure and risk by personal activity time- and population-weighting. A total of 287 PM2.5 samples were collected at ten sites in 2018-2020, and toxic species including heavy metals (HMs), polycyclic aromatic hydrocarbons (PAHs) and organophosphate esters (OPEs) were analyzed. The percentage non-cancer risk were in the order of traffic emission (48 %) > industrial emission (22 %) > coal combustion (12 %) > waste incineration (11 %) > resuspend dust (7 %) > OPE-related products (0 %) ≈ secondary particles (0 %). Similar orders were observed in cancer risk. For traffic emission, due to its higher source contributions and large population in central area, non-cancer and cancer risk fraction increased from 23 % to 48 % and 20 % to 46 % after exposure estimation; while for industrial emission, higher source contributions but small population in suburb area decreased the percentage non-cancer and cancer risk from 38 % to 22 % and 39 % to 24 %, respectively.

Short-term effects of air pollution on hospitalization of children with acute upper respiratory infections: A time series analysis in Lanzhou, China

OBJECTIVE

Short-term air pollution exposure is correlated with childhood acute upper respiratory infections (AURI) hospitalizations. We surveyed the relationship between AURI hospitalizations and air pollutant concentrations in children aged 0-14 years from 1 January 2014 to 31 December 2019 in Lanzhou City.

METHODS

We collected both data on air pollutant concentrations and children's AURI hospitalizations during the study period. Distributional lagged nonlinear models were adopted to assess the short-term effects of air pollutants on children's AURI hospitalizations. We also performed subgroup analysis and sensitivity analysis.

RESULTS

A total of 15,881 children were hospitalized for AURI during the study period. The results showed that for each 10 µg/m3 increase in PM2.5 , SO2 , and NO2 concentrations at lag0-6, the relative risk (RR) values for children hospitalized for AURI were RR = 1.0247 (95% CI: 1.0092,1.0405), RR = 1.0928 (95% CI: 1.0562, 1.1308), and RR = 1.0715 (95% CI: 1.0495, 1.0940), respectively. PM10 was significantly associated with AURI hospitalization in children only at lag0, RR = 1.0028 (95% CI: 1.0000, 1.0056).

CONCLUSION

Short-term exposures to PM2.5 , PM10 , SO2 , and NO2 all increase the risk of AURI hospitalization in children variously.

Towards sustainable and net-zero cities: A review of environmental modelling and monitoring tools for optimizing emissions reduction strategies for improved air quality in urban areas

Climate change is a defining challenge for today's society and its consequences pose a great threat to humanity. Cities are major contributors to climate change, accounting for over 70% of global greenhouse gas emissions. With urbanization occurring at a rapid rate worldwide, cities will play a key role in mitigating emissions and addressing climate change. Greenhouse gas emissions are strongly interlinked with air quality as they share emission sources. Consequently, there is a great opportunity to develop policies which maximize the co-benefits of emissions reductions on air quality and health. As such, a narrative meta-review is conducted to highlight state-of-the-art monitoring and modelling tools which can inform and monitor progress towards greenhouse gas emission and air pollution reduction targets. Urban greenspace will play an important role in the transition to net-zero as it promotes sustainable and active transport modes. Therefore, we explore advancements in urban greenspace quantification methods which can aid strategic developments. There is great potential to harness technological advancements to better understand the impact of greenhouse gas reduction strategies on air quality and subsequently inform the optimal design of these strategies going forward. An integrated approach to greenhouse gas emission and air pollution reduction will create sustainable, net-zero and healthy future cities.

Associations between air pollutant and pneumonia and asthma requiring hospitalization among children aged under 5 years in Ningbo, 2015-2017

Introduction

Exposure to ambient air pollutants is associated with an increased incidence of respiratory diseases such as pneumonia and asthma, especially in younger children. We investigated the relationship between rates of hospitalization of children aged under 5 years for pneumonia and asthma and the concentration of air pollutants in Ningbo between January 1, 2015 and August 29, 2017.

Methods

Data were obtained from the Ningbo Air Quality Data Real-time Publishing System and the big data platform of the Ningbo Health Information Center. A generalized additive model was established via logarithmic link function and utilized to evaluate the effect of pollutant concentration on lag dimension and perform sensitivity analysis.

Results

A total of 10,301 cases of pneumonia and 115 cases of asthma were identified over the course of this study. Results revealed that PM2.5, PM10, SO2 and NO2 were significantly associated with hospitalization for pneumonia and asthma in children under 5 years of age. For every 10-unit increase in lag03 air pollutant concentration, hospitalization for pneumonia and asthma due to PM2.5, PM10, SO2 and NO2 increased by 2.22% (95%CI: 0.64%, 3.82%), 1.94% (95%CI: 0.85%, 3.04%), 11.21% (95%CI: 4.70%, 18.10%) and 5.42% (95%CI: 3.07%, 7.82%), respectively.

Discussion

Adverse effects of air pollutants were found to be more severe in children aged 1 to 5 years and adverse effects due to PM2.5, PM10 and SO2 were found to be more severe in girls. Our findings underscore the need for implementation of effective public health measures to urgently improve air quality and reduce pediatric hospitalizations due to respiratory illness.

Outdoor particulate matter exposure and upper respiratory tract infections in children and adolescents: A systematic review and meta-analysis

BACKGROUND

While the relationship between outdoor particulate matter (PM) and lower respiratory tract infections in children and adolescents is accepted, we know little about the impacts of outdoor PM on the risk of developing or aggravating upper respiratory tract infections (URTIs).

METHODS

We aimed to review the literature examining the relationship between outdoor PM exposure and URTIs in children and adolescents. A systematic search of EMBASE, MEDLINE, PubMed, Scopus, CINAHL and Web of Science databases was undertaken on April 3, 2020 and October 27, 2021. Comparable short-term studies of time-series or case-crossover designs were pooled in meta-analyses using random-effects models, while the remainder of studies were combined in a narrative analysis. Quality, risk of bias and level of evidence for health effects were appraised using a combination of emerging frameworks in environmental health.

RESULTS

Out of 1366 articles identified, 34 were included in the systematic review and 16 of these were included in meta-analyses. Both PM_2.5 and PM₁₀ levels were associated with hospital presentations for URTIs (PM_2.5: RR = 1.010, 95%CI = 1.007-1.014; PM₁₀: RR = 1.016, 95%CI = 1.011-1.021) in the meta-analyses. Narrative analysis found unequivocally that total suspended particulates were associated with URTIs, but mixed results were found for PM_2.5 and PM₁₀ in both younger and older children.

CONCLUSION

This study found some evidence of associations between PM and URTIs in children and adolescents, the relationship strength increased with PM₁₀. However, the number of studies was limited and heterogeneity was considerable, thus there is a need for further studies, especially studies assessing long-term exposure and comparing sources.

Association between Ambient Air Pollution and Emergency Room Visits for Pediatric Respiratory Diseases: The Impact of COVID-19 Pandemic

The level and composition of air pollution have changed during the coronavirus disease 2019 (COVID-19) pandemic. However, the association between air pollution and pediatric respiratory disease emergency department (ED) visits during the COVID-19 pandemic remains unclear. The study was retrospectively conducted between 2017 and 2020 in Kaohsiung, Taiwan, from 1 January 2020 to 1 May 2020, defined as the period of the COVID-19 pandemic, and 1 January 2017 to 31 May 2019, defined as the pre-COVID-19 pandemic period. We enrolled patients under 17 years old who visited the ED in a medical center and were diagnosed with respiratory diseases such as pneumonia, asthma, bronchitis, and acute pharyngitis. Measurements of particulate matter (PM) with aerodynamic diameters of <10 μm (PM10) and < 2.5 μm (PM2.5), nitrogen dioxide (NO2), and Ozone (O3) were collected. During the COVID-19 pandemic, an increase in the interquartile range of PM2.5, PM10, and NO2 levels was associated with increases of 72.5% (95% confidence interval [CI], 50.5−97.7%), 98.0% (95% CI, 70.7−129.6%), and 54.7% (95% CI, 38.7−72.6%), respectively, in the risk of pediatric respiratory disease ED visits on lag 1, which were greater than those in the pre-COVID-19 pandemic period. After adjusting for temperature and humidity, the risk of pediatric respiratory diseases after exposure to PM2.5 (inter p = 0.001) and PM10 (inter p < 0.001) was higher during the COVID-19 pandemic. PM2.5, PM10, and NO2 may play important roles in pediatric respiratory events in Kaohsiung, Taiwan. Compared with the pre-COVID-19 pandemic period, the levels of PM2.5 and PM10 were lower; however, the levels were related to a greater increase in ED during the COVID-19 pandemic.

Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA

A number of studies have found differing associations of disease outcomes with PM_2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO₂, NO_x, O₃, and SO₂), total PM_2.5 mass, and speciated PM_2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM_2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM_2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

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.

Respiratory Emergency Department Visits Associations with Exposures to PM2.5 Mass, Constituents, and Sources in Dhaka, Bangladesh Air Pollution

RATIONALE

To date, there is no published local epidemiological evidence documenting the respiratory health effects of source specific air pollution in South Asia, where PM2.5 composition is different from past studies. Differences include more biomass and residue crop-burning emissions, which may have differing health implications.

OBJECTIVES

We assessed PM2.5 associations with respiratory emergency department (ED) visits in a biomass-burning dominated high pollution region, and evaluated their variability by pollution source and composition.

METHODS

Time-series regression modeling was applied to daily ED visits from January 2014 through December 2017. Air pollutant effect sizes were estimated after addressing long-term trends and seasonality, day-of-week, holidays, relative humidity, ambient temperature, and the effect modification by season, age, and sex.

RESULTS

PM2.5 yielded a significant association with increased respiratory ED visits [0.84% (95% CI: 0.33%, 1.35%)] per 10 μg/m3 increase. The PM2.5 health effect size varied with season, the highest being during monsoon season, when fossil-fuel combustion sources dominated exposures. Results from a source-specific health effect analysis was also consistent with fossil-fuel PM2.5 having a larger effect size per 10 μg/m3 than PM2.5 from other sources [fossil-fuel PM2.5: 2.79% (0.33% to 5.31%), biomass-burning PM2.5: 1.27% (0% to 2.54%), and other-PM2.5: 0.95% (0.06% to 1.85%)]. Age-specific associations varied, with children and older adults being disproportionately affected by the air pollution, especially by the combustion-related particles.

CONCLUSIONS

This study provided novel and important evidence that respiratory health in Dhaka is significantly affected by particle air pollution, with a greater health impact by fossil-fuel combustion derived PM2.5.

High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM2.5 Distribution in Beijing, China

PM2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R2 of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.

High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019

Exposure to fine particulate matter (PM_2.5) has become a major global health concern. Although modeling exposure to PM_2.5 has been examined in China, accurate long-term assessment of PM_2.5 exposure with high spatiotemporal resolution at the national scale is still challenging. We aimed to establish a hybrid spatiotemporal modeling framework for PM_2.5 in China that incorporated extensive predictor variables (satellite, chemical transport model, geographic, and meteorological data) and advanced machine learning methods to support long-term and short-term health studies. The modeling framework included three stages: (1) filling satellite aerosol optical depth (AOD) missing values; (2) modeling 1 km × 1 km daily PM_2.5 concentrations at a national scale using extensive covariates; and (3) downscaling daily PM_2.5 predictions to 100-m resolution at a city scale. We achieved good model performances with spatial cross-validation (CV) R² of 0.92 and temporal CV R² of 0.85 at the air quality sites across the country. We then estimated daily PM_2.5 concentrations in China from 2013 to 2019 at 1 km × 1 km grid cells. The downscaled predictions at 100 m resolution greatly improved the spatial variation of PM_2.5 concentrations at the city scale. The framework and data set generated in this study could be useful to PM_2.5 exposure assessment and epidemiological studies.

Seasonal Variations of Fine Particulate Matter and Mortality Rate in Seoul, Korea with a Focus on the Short-Term Impact of Meteorological Extremes on Human Health

Rapid industrialization of Korea’s economy has brought with it environmental pollution that threatens human health. Among various other pollutants, ambient fine particulate matter known to endanger human health often exceeds air quality standards in Seoul, South Korea’s capital. The goal of this research is to find the impact of meteorological extremes and particle levels on human health. The analysis was conducted using hourly air pollutant concentrations, meteorological variables, and the daily mortality from cerebrovascular disease. Results show that the effect of fine particulate matter on mortality from cerebrovascular disease was more noticeable during meteorological extremes. The linkage between extreme weather conditions and mortality was more apparent in winter than in summer. Comprehensive studies of various causes of diseases should be continued to more accurately analyze the effects of fine particulate matter on human health and meteorological extremes, and to further minimize the public health impact of air pollution and meteorological conditions.

Farnesoid X receptor mediates hepatic steatosis induced by PM2.5

Ambient particulate matter (PM) newly has been regarded as a conceivable hazard for public health. A large number of studies have described that PM, exceptionally PM2.5, is correlated with respiratory, cardiovascular, and metabolic diseases, etc. PM2.5-induced hepatocyte steatosis previously has been uncovered both in cellular and murine models. Nevertheless, less is known about the underlying mechanism. Here, we found that PM2.5 could cause the downregulation of farnesoid X receptor (FXR), a key transcription factor for lipid metabolism. FXR could regulate the accumulation of lipid droplets induced by PM2.5 in vitro. Moreover, FXR-/- mice were exposed to PM2.5 for 2 months to investigate the role of FXR in pathogenesis of PM2.5-induced hepatic steatosis in vivo. The results showed that exposure of wild-type (WT) mice to PM2.5 caused mild liver steatosis compared with the mice exposure to filtered air (FA). Furthermore, the content of triglyceride (TG) and total cholesterol (TC) was elevated in WT mice liver triggered by the inhalation of PM2.5. However, there was no statistical difference in TG and TC content between FXR-/- mice with and without PM2.5 exposure. Overall, our finding suggested FXR mediated PM2.5-induced hepatic steatosis.