High PM2 Research Articles

Development of a monthly PM2.5 forecast model for Seoul, Korea, based on the dynamic climate forecast and a convolutional neural network algorithm

A convolutional neural network (CNN) model has been developed to predict monthly concentrations of particulate matter with a diameter ≤ 2.5 μm (PM2.5) in Seoul, Republic of Korea, during winter months (November through February). Seven meteorological variables influencing PM2.5 concentrations were selected as predictors: geopotential heights at 1000 and 500 hPa, zonal and meridional winds, relative humidity and temperature at 850 hPa, and boundary layer height. These predictors were obtained from the fifth generation of the European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) for model construction and from the Korea Polar Prediction System (KPOPS) dynamic forecasts for operational predictions. From the 11-year ERA5 dataset for 2008–2020, one year was allocated for testing, while the remaining 10 years were randomly assigned for either training (80%) or validation (20%). This process was repeated until all 11 years were allocated for testing, resulting in 11 CNN models. The benchmark which used the ERA5 dataset as the predictors yielded a mean bias error (MBE) of 0.05 μg m−3, root mean square error (RMSE) of 2.41 μg m−3, Pearson correlation coefficient (R) of 0.85, and 71% hit rate in predicting the months of high PM2.5 concentrations (≥ 30 μg m−3) for the testing periods. The 11-CNN-model ensemble predictions using the KPOPS forecasts resulted in an MBE of 0.1 μg m−3, RMSE of 3.19 μg m−3, R of 0.74, and hit rate of 62.5% for the period 2008–2020. Considering that the average temporal correlation coefficients between the ERA5 and the KPOPS forecasts for the seven predictors ranged from 0.20 to 0.66, the CNN-based PM2.5 prediction model has demonstrated good performance in overcoming the adverse effects of the KPOPS forecast errors on predicting PM2.5 concentrations. The CNN model showed limitations for unusual period such as the COVID-19 period, November 2020 through February 2022, when the emissions in East Asia were substantially below their climatological normal levels. In conclusion, the CNN model developed in this study showed potential applicability to operational monthly PM2.5 concentration predictions for the years of normal emissions using solely meteorological data. This can provide professionals in various fields, such as health care and environmental science, with valuable insights for long-term planning.

Distribution of Fine Particulate Matter Pollution in Winter over Eastern China Affected by Synoptic Conditions

Based on the mean sea level pressure field and 10-meter wind field across eastern China, weather patterns were classified using principal component analysis in the T-model (T-PCA), and four weather conditions were identified. Weather conditions and meteorological factors affecting the winter PM2.5 concentration in the Beijing–Tianjin–Hebei (BTH) region and the Yangtze River Delta (YRD) were also analyzed. The results showed that there were significant differences in the PM2.5 distribution between BTH and the YRD under different weather conditions. The intensity and path of cold air played important roles in regulating the PM2.5 concentration distribution in eastern China. For the BTH region, under type 2 and type 4 conditions, the weather was stable, and heavy pollution frequently occurred; however, under type 1 and type 3 conditions, cold air was active, and the air quality improved. For the YRD, both type 1 and type 4 conditions lead to high PM2.5 concentrations in this region. Type 1 cold air flows southward along the northwestern path and is beneficial for transporting pollutants from BTH to the YRD, causing a high probability of PM2.5 pollution. Conversely, the southward movement of type 3 cold air along the eastern path was beneficial for pollutant diffusion in the YRD.

Particulate Matter Exposure and Default Mode Network Equilibrium During Early Adolescence.

Background: Air pollution exposure has been associated with adverse cognitive and mental health outcomes in children, adolescents, and adults, although youth may be particularly susceptible given ongoing brain development. However, the neurodevelopmental mechanisms underlying the associations among air pollution, cognition, and mental health remain unclear. We examined the impact of particulate matter (PM2.5) on resting-state functional connectivity (rsFC) of the default mode network (DMN) and three key attention networks: dorsal attention, ventral attention, and cingulo-opercular. Methods: Longitudinal changes in rsFC within/between networks were assessed from baseline (9-10 years) to the 2-year follow-up (11-12 years) in 10,072 youth (M ± SD = 9.93 + 0.63 years; 49% female) from the Adolescent Brain Cognitive Development (ABCD®) study. Annual ambient PM2.5 concentrations from the 2016 calendar year were estimated using hybrid ensemble spatiotemporal models. RsFC was estimated using functional neuroimaging. Linear mixed models were used to test associations between PM2.5 and change in rsFC over time while adjusting for relevant covariates (e.g., age, sex, race/ethnicity, parental education, and family income) and other air pollutants (O3, NO2). Results: A PM2.5 × time interaction was significant for within-network rsFC of the DMN such that higher PM2.5 concentrations were associated with a smaller increase in rsFC over time. Further, significant PM2.5 × time interactions were observed for between-network rsFC of the DMN and all three attention networks, with varied directionality. Conclusion: PM2.5 exposure was associated with alterations in the development and equilibrium of the DMN-a network implicated in self-referential processing-and anticorrelated attention networks, which may impact trajectories of cognitive and mental health symptoms across adolescence.

The impact of air pollution on the facial skin of Caucasian women using real-life pollutant exposure measurements.

To date, studies examining the effect of air pollution on skin characteristics have relied on regional pollution estimates obtained from fixed monitoring sites. Hence, there remains a need to characterize the impact of air pollution in vivo in real-time conditions. We conducted an initial investigation under real-life conditions, with the purpose of characterizing the in vivo impact of various pollutants on the facial skin condition of women living in Paris over a 6-month period. A smartphone application linked to the Breezometer platform was used to collect participants' individual exposures to pollutants through the recovery of global positioning system (GPS) data over a 6-month period. Daily exposure to fine particulate matter (PM 2.5µm and PM 10µm), pollen, and air quality was measured. Facial skin color, roughness, pore, hydration, elasticity, and wrinkle measurements were taken at the end of the 6-month period. Participants' cumulated pollutant exposure over 6 months was calculated. Data were stratified into two groups (lower vs. higher pollutant exposure) for each pollutant. 156 women (20-60 years-old) were recruited, with 124 women completing the study. Higher PM 2.5µm exposure was associated with altered skin color and increased roughness under the eye. Higher PM 10µm exposure with increased wrinkles and roughness under the eye, increased pore appearance, and decreased skin hydration. Exposure to poorer air quality was linked with increased forehead wrinkles and decreased skin elasticity, while higher pollen exposure increased skin roughness and crow's feet. This study suggests a potential correlation between air pollution and facial skin in real-life conditions. Prolonged exposure to PM, gases, and pollen may be linked to clinical signs of skin ageing. This study highlights the importance of longer monitoring over time in real conditions to characterize the effect of pollution on the skin.

Home and school pollutant exposure, respiratory outcomes, and influence of historical redlining

BackgroundThe discriminatory and racist policy of historical redlining in the United States (U.S.) during the 1930s played a role in perpetuating contemporary environmental health disparities. ObjectiveOur objectives were to determine associations between home and school pollutant exposure (fine particulate matter (PM2.5), nitrogen dioxide (NO2)) and respiratory outcomes (Composite Asthma Severity Index (CASI), lung function) among school-aged children with asthma and examine whether associations differed between children who resided and/or attended school in historically redlined compared to non-redlined neighborhoods. MethodsChildren ages 6 to 17 with moderate-to-severe asthma (N=240) from 9 U.S. cities were included. Combined home and school exposure to PM2.5 and NO2 was calculated based on geospatially assessed monthly averaged outdoor pollutant concentrations. Repeated measures of CASI and lung function were collected. ResultsOverall, 37.5% of children resided and/or attended schools in historically redlined neighborhoods. Children in historically redlined neighborhoods had greater exposure to NO2 (median: 15.4 vs 12.1 ppb) and closer distance to a highway (median: 0.86 vs 1.23 km), compared to those in non-redlined neighborhoods (p<0.01). Overall, PM2.5 was not associated with asthma severity or lung function. However, among children in redlined neighborhoods, higher PM2.5 was associated with worse asthma severity (p<0.005). No association was observed between pollutants and lung function or asthma severity among children in non-redlined neighborhoods (p>0.005). ConclusionsOur findings highlight the significance of historical redlining and current environmental health disparities among school-aged children with asthma, specifically, the environmental injustice of PM2.5 exposure and its associations with respiratory health.

Particulate Matter Capture and Air Pollution Tolerance of Six Roadside Plants in Cheongju, South Korea

Particulate matter (PM), a highly hazardous air pollutant with known adverse health effects, has been proven to be effectively mitigated by using plants. This study aimed to evaluate the capacity of various plants in capturing PM and their tolerance to air pollution. This will facilitate the selection of suitable species for roadside planting. Accordingly, this research quantified the accumulation of particulate matter in six different plant species. Four biochemical parameters [total chlorophyll, leaf pH extract, relative water content, and ascorbic acid] were evaluated to determine the Air Pollution Tolerance Index (APTI), PM accumulation results varied among the plant species. The tolerance level was categorized into four groups: tolerant, moderately tolerant, intermediate, and sensitive. Ligustrum obtusifolium demonstrated the highest PM accumulation and tolerance index values. The presence of leaf hair and roughness on leaf surfaces was observed to facilitate higher PM accumulation in certain plants. PM accumulation on leaves was significantly correlated with biochemical parameters and APTI of plants. Based on these outcomes, Ligustrum obtusifolium, Hibiscus syriacus, and Chamaecyparis obtusa were identified as suitable for roadside planting to mitigate atmospheric particulate matter.

Chemical characteristics and formation mechanism of secondary inorganic aerosols: The decisive role of aerosol acidity and meteorological conditions

In recent years, there has been a growing concern about air pollution and its impact on the air quality and human health, especially for fine particulate matter (PM2.5) and its associated secondary aerosols in urban areas. This study conducted a year-long field campaign to collect PM2.5 samples day and night in an urban area of central Taiwan. Higher PM2.5 mass concentrations were observed in winter (27.7 ± 9.7 μg/m3), followed by autumn (22.5 ± 8.3 μg/m3), spring (19.2 ± 6.4 μg/m3), and summer (11.0 ± 3.1 μg/m3). The dominant formation mechanism of secondary inorganic aerosols was heterogeneous reactions of NO3− at night and homogeneous reactions of SO42− during the day. Additionally, significant correlations were observed between aerosol liquid water content (ALWC) and NO3− during nighttime, indicating the importance of aqueous-phase NO3− formation. The role of aerosol acidity was explored and a unique alkaline condition was found in spring and summer, which showed lower PM2.5 concentrations than the neutralized condition. Under the neutralized condition, higher PM2.5 concentrations were commonly found when combining the ammonium-rich regime with molar ratios of [NO3−]/[SO42−] exceeding 1.6, suggesting the importance of reducing both NH3 and NOx. Furthermore, the results showed that reducing NH3 should be prioritized under high temperature conditions, while reducing NOx became important under low temperature conditions. Clustering of backward trajectories showed that long-range transport could enhance the formation of secondary aerosols, but local emissions emerged as the main factor driving high PM2.5 concentrations. This study provides insights for policymakers to improve air quality, suggesting that different mitigation strategies should be formulated based on meteorological variables and that using clean energy for vehicles and electricity generation is important to alleviate air pollution.

Time series analysis of COVID-19 stringency measures on the spatio- temporal dynamics of air pollution

Introduction: Execution of COVID-19 lockdown measures caused variations in air pollution worldwide. This paper investigates the impact of COVID-19 stringency measures on the spatio-temporal dynamics of air pollution in Mumbai, India, using a comprehensive two-and-a-half-year pandemic period dataset. Materials and methods: We classified the pandemic period into 7 phases and 21 sub-phases based on the severity of the Oxford COVID-19 Government Response Tracker (OxCGRT) Stringency Index (SI). Optimized Hotspot analysis (OHS) and Ordinary Least Square Regression models explored the spatio-temporal fluctuations and the effect of stringency measures on air quality. Results: The R2 value varied; with the best model R2 of 0.61 for Particulate Matters (PM10) and Nitrogen dioxide (NO2) and lowest of 0.23 for Sulfur dioxide (SO2). A 10-point increase in SI caused a 3-7% reduction in air pollutants. Substantial reduction in average PM10, PM2.5, NO2, and Carbon monoxide (CO) was observed throughout the COVID-19 phases. Meteorology and SI collectively caused maximum reduction of 82.6%, 72.7%, 53.8%, 52.2%, 49.1%, 28.4% for NO2, PM2.5, PM10, NH3, CO, and SO2 respectively, during complete or extreme lockdown phases. Except SO2, seasonality significantly influenced the pollutant concentrations. Winter was the worst period while monsoon was the best. OHS identified central Mumbai wards as hotspots and areas close to the national park as coldspots. Conclusion: PM10, NO2 and CO were more affected by SI measures than NH3 and SO2. For a rapid emergency response to high PM10, implementation of SI, very high (≥ 80 score) and above is advised. Findings of this study have significant public health policy implications, especially among global south nations.

The seasonal variation of Asian dust, anthropogenic PM, and their sources in Dushanbe, Tajikistan

Central Asia's arid climate and growing population make it susceptible to air pollution from mineral dust and anthropogenic sources. To assess the impact of these pollution sources on local air quality, the chemical composition of PM10 aerosol particles was collected within a year at Dushanbe, Tajikistan. Primary sources of the pollutants, their impact on PM10 composition, and their seasonal trends were assessed. The measurement approach employed analysis of trace metals, ions, organic compounds, laboratory investigations, diagnostic ratio calculations, all complemented by positive matrix factor analysis. The results reveal that for about 70% of the year, PM10 levels (80 ± 55 μg m−3 as the annual average) in Dushanbe were higher than the WHO limits, with varying levels during the seasons. Summer concentrations peaked up to 434 μg m−3 due to a combined mineral and road dust influence. Interestingly, winter concentrations reached 152 μg m−3 due to strong contributions (about 64%) from residential heating and fossil fuel combustion, often triggered during sub-10 °C temperatures. Mineral dust events were associated with elevated concentrations of trace metals, including Zn and Pb, alongside organic carbon and high-chain n - alkanes. During such events, Ca/Fe, Ca/Al ratios exceeded those of Saharan dust, suggesting a high salt content in Asian dust, potentially influenced by dried-off lake emissions. It was striking to observe that winter periods exhibited concerning levels of carcinogenic PAHs, such as benzo(b)fluoranthene (<25 ng m−3) and benzo[a]pyrene (<12 ng m−3, BaP), with average values of 4.1 ± 5.9 ng m−3 (BaP) that significantly exceed WHO recommendations. The high PM10 and PAH exposure could pose severe health risks, necessitating collective attention from the local authorities in developing new mitigation strategies concerning the reduction of emissions from residential heating of coal and wood in winter to curb combustion emissions and reduce exposure.

Enhancing fine particle removal by electrostatic precipitation from flue gas with a high PM concentration: Effect of various electrode configurations

Particle pollution has hazardous effects on air quality and human health, especially fine particle. However, the highly efficient removal of fine particles is an unsolved problem in electrostatic precipitators (ESPs). In this study, a transparent ESP was designed to investigate the effect of fine particles on the corona discharge performance and the role of ionic wind in particle migration. And five types of typical discharge electrodes were applied. The results showed that the presence of fine particles could weaken the corona current produced by electrode discharge. Among these electrodes, the spike electrode can reduce the adverse effect of the particle space charge. The maximum ionic wind velocity around the electrode tips and within the near-plate region could reached 5.67 m/s and 5.00 m/s under an applied voltage of 30 kV, respectively. Combining the effects of corona discharge and ionic wind, the collection efficiencies of fine particles decreased in the following order: spike electrode (medium needle) > spike electrode (longest needle) > arista electrode > spike electrode (shortest needle) > sawtooth electrode. In general, spike electrode discharge has better performance for particle charging and particle collection efficiency and is suitable for fine particle removal in ESPs. In addition, electrode configuration/arrangement optimization methods were proposed to enhance fine particle removal in ESPs.

Daytime and night-time patterns in PM2.5 concentrations over Palembang City of Indonesia using MERRA-2 reanalysis data

ABSTRACT Palembang has sustained heavy haze pollution events that are related to decrease public health quality. Because ground-based PM2.5 observation in the city has some limitations, such as spatial and temporal coverages. Thus, in this study, we applied the potential of using the surface mass concentration PM2.5 data acquired by the MERRA-2 satellite to predict daytime and night-time PM2.5 levels during heavy haze events in Palembang City. Results of the study revealed that the highest PM2.5 level was about 160 µg/m3 at 02:00 (night-time), while the lowest PM2.5 level was about 50 µg/m3 at 14:00 (daytime). Spatiotemporal variation of PM2.5 over the study area showed that more than half of the area showed PM2.5 above 90 µg/m3 at night. Stable wind conditions (2 m/s) and air temperature (29°C), and relative humidity (74%) in the study area have contributed to particulate matter accumulation. The study concluded that PM2.5 acquired by the MERRA-2 satellite could provide a new reliable source for assessing severe air pollution events.

The association between the scarlet fever and meteorological factors, air pollutants and their interactions in children in northwest China.

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10μm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation.

Temporal variations and chemical characteristics of marine PM2.5 at Dongsha Islands, South China Sea: Three-year measurement

The study of long-range transport effects on marine fine particles (PM2.5), particularly in remote sites such as the Dongsha Islands, is pivotal for advancing our understanding of air pollution dynamics on a regional scale and for formulating effective environmental policies. PM2.5 concentrations were examined over three consecutive years and grouped based on their transport routes. The backward trajectory simulation revealed that high PM2.5 concentrations were observed in the West Channel, originating from North and Central China, the Korean Peninsula, and the Japanese Islands, opposed to the East Channel. High PM2.5 concentrations, commonly observed in winter and spring, were mainly attributed to the Asian Northeastern Monsoons. Water-soluble inorganic ions constituted the major components, accounting for 37.8–48.7% of PM2.5, and followed by metal elements (15.5–20.0%), carbons (7.5–13.3%), levoglucosan (0.01–0.17%), and organic aerosols (0.2–2.2%). Secondary inorganic aerosols as the dominant source accounted for 8.3–24.7% of PM2.5, while sea salts were the secondary major contributor. High levoglucosan contribution (3.8–7.2%) in winter and spring was attributed to biomass burning, mainly from the Indochina Peninsula. Chemical mass balance receptor modeling resolved that major sources of PM2.5 were secondary sulfate, sea salts, fugitive dust, and industrial boilers. This study concluded that the long-range transport of PM2.5 gradually increased since fall, contributing 52.1–74.3%, highlighting its substantial impact on PM2.5 in all seasons except summer.

Ambient air pollution and urological cancer risk: A systematic review and meta-analysis of epidemiological evidence

Exposure to ambient air pollution has significant adverse health effects; however, whether air pollution is associated with urological cancer is largely unknown. We conduct a systematic review and meta-analysis with epidemiological studies, showing that a 5 μg/m3 increase in PM2.5 exposure is associated with a 6%, 7%, and 9%, increased risk of overall urological, bladder, and kidney cancer, respectively; and a 10 μg/m3 increase in NO2 is linked to a 3%, 4%, and 4% higher risk of overall urological, bladder, and prostate cancer, respectively. Were these associations to reflect causal relationships, lowering PM2.5 levels to 5.8 μg/m3 could reduce the age-standardized rate of urological cancer by 1.5 ~ 27/100,000 across the 15 countries with the highest PM2.5 level from the top 30 countries with the highest urological cancer burden. Implementing global health policies that can improve air quality could potentially reduce the risk of urologic cancer and alleviate its burden.

Spatiotemporal distribution of PM2.5 and health risk assessment in Kolkata, India: Evaluation of non-carcinogenic health hazards and premature mortality

The State of Global Air Report (2022) says that Kolkata is the second most polluted city worldwide, with continuously high PM2.5 concentrations. This study examines the spatial distribution of PM2.5 and its associated health risks in the region. The yearly average concentrations of PM2.5 were found to consistently surpass the National Ambient Air Quality Standard (NAAQS) levels in India, China, United States of America (USA), and Canada including the prescribed standards of World Health Organization (WHO) and European Union (EU). Additionally, PM2.5 levels exceeded the 24-h NAAQS for a substantial number of days in a year. The study reveals significant correlation between meteorological factors and PM2.5, with temperature playing major role in the distribution of PM2.5. The hazard quotient (HQ) values continuously exceeded the threshold level, indicating severity of air pollution. Long-term exposure to PM2.5 resulted in a significant number of premature deaths, with ischemic heart disease (IHD) being the leading cause in Kolkata. The study indicates that reducing PM2.5 levels has a positive impact on mortality rates and health outcomes. For every unit reduction (1 μg/m3) of PM2.5 concentration, the corresponding decrease in premature mortality by 2.40%. Furthermore, reductions of 5 μg/m3 and 10 μg/m3 were associated with health improvements of 13.85% and 33.40% respectively. Overall, this study provides valuable information to effectively regulate air quality, implement targeted interventions, and protect public health.

Mesoscale wind field patterns conducive to the high-PM2.5 episodes over South Korea: Cluster analysis

Mesoscale wind field patterns play important roles in episodes of high particulate matter (PM2.5). Previous studies have reported the main synoptic meteorological features associated with high-PM2.5 days with two primary modes: long-range transport (LRT) and stagnation or local contribution (STL). However, due to the lack of more small-scale analyses spanning across multiple administrative regions, statistical quantification of representative mesoscale wind patterns with elevated PM2.5 episodes is needed to explore region-specific emissions reduction strategies in South Korea. This study applied k-means clustering to NCEP FNL reanalysis data from 2016 to 2020 to identify mesoscale wind field patterns associated with high-PM2.5 episodes across all 17 administrative regions of South Korea. Our statistical approach yielded region-specific wind fields for 17 individual administrative regions, and also empirically yielded a total of eight representative patterns, four patterns each for both LRT and STL modes, which were applicable across all regions. The eight patterns were consistent with most previous studies. In addition, we quantified the LTP and STL modes in all 17 regions, and showed that they were more prevalent in the western and southeastern administrative regions of South Korea, respectively. Our results serve as a reference for region-specific mitigation strategies in all administrative regions of South Korea, and will also be useful for analyzing relations between meteorology and high-PM2.5 episodes in the SIJAQ and ASIA-AQ studies.

Socioeconomic and sociodemographic correlations to COVID-19 variability in the United States in 2020.

The COVID-19 pandemic provided an additional spotlight on the longstanding socioeconomic/health impacts of redlining and has added to the myriad of environmental justice issues, which has caused significant loss of life, health, and productive work. The Centers for Disease Control and Prevention (CDC) reports that a person with any selected underlying health conditions is more likely to experience severe COVID-19 symptoms, with more than 81% of COVID-19-related deaths among people aged 65 years and older. The effects of COVID-19 are not homogeneous across populations, varying by socioeconomic status, PM2.5 exposure, and geographic location. This variability is supported by analysis of existing data as a function of the number of cases and deaths per capita/1,00,000 persons. We investigate the degree of correlation between these parameters, excluding health conditions and age. We found that socioeconomic variables alone contribute to ~40% of COVID-19 variability, while socioeconomic parameters, combined with political affiliation, geographic location, and PM2.5 exposure levels, can explain ~60% of COVID-19 variability per capita when using an OLS regression model; socioeconomic factors contribute ~28% to COVID-19-related deaths. Using spatial coordinates in a Random Forest (RF) regressor model significantly improves prediction accuracy by ~120%. Data visualization products reinforce the fact that the number of COVID-19 deaths represents 1% of COVID-19 cases in the US and globally. A larger number of democratic voters, larger per-capita income, and age >65 years is negatively correlated (associated with a decrease) with the number of COVID cases per capita. Several distinct regions of negative and positive correlations are apparent, which are dominated by two major regions of anticorrelation: (1) the West Coast, which exhibits high PM2.5 concentrations and fewer COVID-19 cases; and (2) the middle portion of the US, showing mostly high number of COVID-19 cases and low PM2.5 concentrations. This paper underscores the importance of exercising caution and prudence when making definitive causal statements about the contribution of air quality constituents (such as PM2.5) and socioeconomic factors to COVID-19 mortality rates. It also highlights the importance of implementing better health/lifestyle practices and examines the impact of COVID-19 on vulnerable populations, particularly regarding preexisting health conditions and age. Although PM2.5 contributes comparable deaths (~7M) per year, globally as smoking cigarettes (~8.5M), quantifying any causal contribution toward COVID-19 is non-trivial, given the primary causes of COVID-19 death and confounding factors. This becomes more complicated as air pollution was reduced significantly during the lockdowns, especially during 2020. This statistical analysis provides a modular framework, that can be further expanded with the context of multilevel analysis (MLA). This study highlights the need to address socioeconomic and environmental disparities to better prepare for future pandemics. By understanding how factors such as socioeconomic status, political affiliation, geographic location, and PM2.5 exposure contribute to the variability in COVID-19 outcomes, policymakers and public health officials can develop targeted strategies to protect vulnerable populations. Implementing improved health and lifestyle practices and mitigating environmental hazards will be essential in reducing the impact of future public health crises on marginalized communities. These insights can guide the development of more resilient and equitable health systems capable of responding effectively to similar future scenarios.

Intercomparison of methods for atmospheric reactive mercury observations: Evidences to interpret what we are actually measuring

Accurate measurements of atmospheric reactive mercury (RM or HgII) including gaseous oxidized mercury (GOM) and particulate-bound mercury (PBM) are crucial to improving understanding of mercury (Hg) behavior in the ambient air and evaluating the effectiveness of the Minamata Convention. As part of the Speciation and Transformation of Atmospheric Mercury in a Polluted region (STAMP) campaign in eastern China, comparison of Tekran, the reactive mercury active system (RMAS) and the micro-orifice uniform deposit impactors (MOUDI) system for RM measurements was conducted in this study. The ratio of GOMTekran/GOMRMAS was found to be positively correlated with the ratio of PBM/GOM and the proportion of [-Br/Cl] in RM based on deconvolution of the RMAS thermal desorption profiles. HgII reduction by the aqueous HO2 radicals was found to be the most likely cause of GOM underestimation by denuder-based methods. PBM acting as a substitute for GOM in HgII reduction could limit the underestimation. High particulate matter (PM) environment could cause PBM breakthrough in RMAS sampling, resulting in PBM underestimation and GOM overestimation. The chemical compound characteristics of RM and HgII distribution on particles by size provide evidences for the sources of the discrepancies in PBM measurements by different methods. Particle-size-resolved compound profiles are useful approaches for accurate RM quantification.

Association between wildfire PM2.5 and genitourinary cancer survival in California.

10567 Background: Aerosolized particulate matter (PM) 2.5 (i.e., particles ≤ 2.5 µm in diameter or fine particles), including those that emanate from wildfires, has been previously associated with cancer risk and survival. While prior studies have focused primarily on etiology, little is known about the effects of wildfire PM2.5 on cancer prognosis. We sought to explore the effects of wildfire PM2.5 on genitourinary (GU) cancer survival in the state of California where episodic wildfires are considered a component of its climate. Methods: Using the California Cancer Registry, we identified incident prostate (n=81,261), urothelial (n=26,689), and kidney (n=26,303) cancers diagnosed between 2017-2020. Daily PM2.5 was estimated over the same period. Random forests regression was used to fuse diverse datasets (official air quality monitoring, satellite observations, meteorological modeling, predictive smoke modeling, and low-cost sensor networks [PurpleAir]) and produce a consensus estimate of PM2.5 for each centroid of a 1-square kilometer grid across California. We used home address at the time of cancer diagnosis to map each cancer case to the nearest centroid, and averaged PM2.5 exposures over the 6 months preceding cancer diagnosis for each case. Cox-proportional hazards regression was used to quantify the hazard ratio (HR) for death from any cause following cancer diagnosis associated with PM2.5, adjusting for key covariates including age, sex, race/ethnicity, neighborhood socioeconomic status, comorbidity, smoking status, rural/urban residence, and stage at diagnosis. Results: In patients with prostate cancer, a 10 µg/m³ increase in PM2.5 concentration was associated with an 18% increased hazard of death (HR=1.18; 95% confidence interval (CI)= 1.02-1.36, p=0.023). When stratified by stage at diagnosis, the association of PM2.5 and survival was limited to stages I, II, and III (HR=1.39; CI= 1.11-1.74, p=0.005) versus stage IV (HR=1.06; CI= 0.87-1.27, p=0.566). For urothelial cancer, a 10 µg/m³ increase in PM2.5 concentration was associated with a non-statistically significant 11% increased hazard of death (HR=1.11; CI= 0.96-1.29, p=0.151), which did not differ by stage at diagnosis. Kidney cancer survival was not associated with PM2.5 (HR= 0.94; CI= 0.79-1.11, p= 0.44). Conclusions: These findings suggest that higher PM2.5 exposure could influence prostate and urothelial cancer prognosis, perhaps through alteration of physiological pathways relevant to cancer aggressiveness or impairing response to treatment, including cellular immune response, inflammation, angiogenesis, or cell-signaling pathways. Further studies should look into the mechanisms and design mitigating strategies in patients diagnosed with these high-risk cancers who have been exposed to wildfire PM2.5.

Investigating the effect of spatial patterns of artificial impervious surface on PM2.5 at the intra-urban scale

ABSTRACT Artificial impervious surface, as the hallmark of urbanization, contributes to urban development but simultaneously leads to urban PM2.5 pollution. However, how artificial impervious surface affected PM2.5 at the intra-urban scale was rarely investigated. By using remote-sensing techniques to derive PM2.5 concentrations and dividing urban areas into two categories with different urban development situation (areas with drastic expansion of artificial impervious surface (DEA) and areas with slight or no changes of artificial impervious surface (SNCA)), we provided a new perspective for investigating the effects of spatial patterns of artificial impervious surface on PM2.5. First, based on multi-source predictors, a two-staged XGBoost model was adopted to derive PM2.5 concentrations. Then, after delineating DEA and SNCA based on the variations of artificial impervious surface and selecting indicators, the overall and local effects of spatial patterns of artificial impervious surface were respectively explored by Original Least Square regression (OLS) and Geographically Weighted Regression (GWR). The results demonstrated that, compared with in SNCA, population and economic development contributed to higher levels of PM2.5 concentrations in DEA. Additionally, in DEA, high PM2.5 concentrations tended to occur in areas with artificial impervious surfaces exhibiting high coverage, complex shape, and uncompact distribution. In SNCA, among all landscape spatial patterns, the effect of artificial impervious surface coverage was the strongest. Spatially, artificial impervious surface coverage and economic development had much more significant impacts on PM2.5 than other indicators. Among these two indicators, the exacerbating effect of artificial impervious surface coverage on PM2.5 pollution was more pronounced in SNCA, while economic development notably brought high PM2.5 concentrations in the suburban districts of DEA. In general, the framework proposed in this study advanced the understanding of the effects of artificial impervious surface on PM2.5 and the findings are valuable for the mitigation of PM2.5 pollution at the intra-urban scale.