A Source Apportionment of U.S. Fine Particulate Matter Air Pollution

Impact of PM2.5 filter extraction solvent on oxidative potential and chemical analysis

Fine particulate matter (PM2.5) is hypothesized to induce oxidative stress, and has been linked to acute and chronic adverse health effects. To better understand the risks and underlying mechanisms following exposure, PM2.5 is collected onto filters but prior to toxicological analysis, particles must be removed from filters. There is no standard method for filter extraction, which creates the possibility that the methods of extraction selected can alter the chemical composition and ultimately the biological implications. In this study, comparisons were made between extraction solvents (methanol (MeOH), dichloromethane (DCM), 0.9% saline, and Milli-Q water) and the results of oxidative potential and elemental concentration analysis of PM2.5 collected across sites in Arkansas, USA. Significant differences were observed between solvents, with DCM having significantly different results compared to all other extraction solvents (p ≤ 0.001). Significant correlations between element, black carbon, and PM2.5 concentrations and oxidative potential were observed. The observed correlations were extraction solvent dependent. For example, in saline extracted samples, oxidative potential had significant negative correlations with: Ba, Cd, Ce, Co, Ga, Mn and significant positive correlations with: Cr, Ni, Th, U. While in MeOH extracted samples, significant positive correlations were only between oxidative potential and Ga, U and significant negative correlations with V. This indicates that PM2.5 samples extracted with different solvents will yield different conclusions about the causal components. This study highlights the importance of filter extraction methods in interpretation of oxidative potential results and comparisons between studies.Implications: While there is no standard method for PM2.5 filter extraction, variation of extraction methods impact analytical results. This project identifies that extraction method variation, particularly extraction solvent selection, leads to discrepancies in chemical and toxicological analysis for PM2.5 collected on the same filter. This work highlights the need for methods standardization to support accurate comparisons between PM2.5 research studies, thus providing better understanding of PM2.5 across the globe.

Metals and metalloids pollution levels, partitioning, and sources in the environmental compartments of a small urban catchment in Moscow megacity

This study examines the contamination levels and sources of 32 metals and metalloids (MMs) in environmental compartments (roadside soil, road dust, and river suspended sediments) of a small urbanized river catchment located in Moscow megacity. MMs partitioning between particle size fractions (PM1000, PM1-10, and PM1) was analyzed by ICP-MS and ICP-AES methods. The pollution level of particle size fractions with MMs decreases in the following series: road dust > suspended sediments > soils. Absolute principal component analysis with multiple linear regression (PCA/APCS-MLR) shows that in both relatively coarse (PM1-10) and fine (PM1) fractions, traffic emissions are the primary contributors to pollution, whereas natural sources are dominant providers of chemical elements in bulk samples (PM1000). The predominance of fractions with a diameter over 10 μm in all three studied compartments indicates that the mineral matrix of all compartments is formed predominantly by natural material. Across all compartments and their fractions, Sb, Cd, Zn, Mo, W, Sn, Cu, Pb, and Bi are consistently accumulated. PM1 and PM1-10 particles of road dust and suspended sediments also absorb Ni and Cr, suspended sediments retain Mn and As, and soils additionally accumulate As. Anthropogenic influence is more pronounced in PM1 and PM1-10 particles compared to bulk samples due to a large impact of industrial sources, traffic, construction activities, and waste storage. Polluted soils are an additional source of MMs to PM1 and PM1-10 of road dust and PM1-10 of suspended sediments, and road dust acts as a source of MMs to PM1-10 of soils.

Regional and seasonal drivers of metals and PAHs concentrations in road dust and their health implications in the Czech Republic

While car exhaust emissions in the EU are clearly decreasing, the future of non-exhaust emissions looks more pessimistic. The relative importance of the latter is thus expected to increase in terms of air quality and human health. The aim of the study was to assess regional and seasonal differences in the chemical composition of road dust across the Czech Republic and the health impact of its resuspension, with special respect to polycyclic aromatic hydrocarbons and metals. The road dust samples across all regions and seasons were collected. Based on subsequent laboratory and statistical processing, the spatiotemporal distribution of elements and PAHs was evaluated. Next, the contribution of road dust resuspension to air concentrations was estimated and related health impacts were assessed. A significant regional and seasonal variations in PAHs and metals were discovered. Air quality, leading to atmospheric deposition, was the most important factor contributing to these variations. In contrast, road traffic intensity played only a minor role in influencing the concentrations of metals and PAHs in road dust. Exposure to the PM10 fraction of road dust led to an increase in premature mortality, postneonatal infant mortality, and the prevalence, occurrence, and incidence of bronchitis by several percent. It also significantly raises the annual rate of emergency respiratory hospitalizations and the number of days per year using bronchodilators. Exposure to PAHs and heavy metals in road dust causes cancer incidence on the order of a few cases per 10 million people. Air quality protection measures that lead to a decrease in atmospheric deposition rates are required for the effective reduction of health risks associated with particle resuspension by traffic.

Effect of traffic volumes on polycyclic aromatic hydrocarbons of particulate matter: A comparative study from urban and rural areas in Malaysia

Motor vehicles emit most Malaysian PAHs in particulate matter of 2.5 μm (PM2.5-bound PAHs). Although traffic-related air pollution harms healthy people, there is a knowledge gap regarding PAHs' effects on Malaysians. This study examines PM2.5-bound PAH concentrations, distribution, sources, and health risks in Malaysia's high and low-traffic zones. Kuala Lumpur (KL) and Hulu Langat (HL) exhibit Malaysia's high- and low-traffic areas. The high-volume air sampler collected 40 ambient PM2.5 samples at both locations. Solid-phase extraction and gas chromatography-mass spectrometry (GC-MS) assessed PAHs. The mean PM2.5-bound PAH concentrations in KL (5.85 ng m-3) were significantly higher than in HL (0.55 ng m-3) (p<0.001). KL has nine times more low-molecular-weight PAHs (LMW-PAHs) (2.63 vs. 0.27 ng m-3) and eleven times more high-molecular-weight PAHs (HMW-PAHs) (3.22 vs. 0.28 ng m-3) than HL. Over 51% of PM2.5 air samples at both sites included HMW-PAHs. Source apportionment tools (Diagnostic ratio, positive matrix factorization, and principal component analysis) showed that fossil fuel combustions (petrol and diesel) produced the greatest PAHs in both locations. Moreover, PAH exposure impinged higher carcinogenic health risks in KL than in HL. In conclusion, traffic and automobile pollution account for the short- and long-term health risks posed by PAHs in both regions.

Contrasting features of winter-time PM2.5 pollution and PM2.5-toxicity based on oxidative potential: A long-term (2016-2023) study over Kolkata megacity at eastern Indo-Gangetic Plain

The present study is an attempt to understand the level of PM2.5 pollution and its toxicity based on the oxidative potential (OP) during the winter-time pollution period over Kolkata, a megacity at the eastern most parts of Indo-Gangetic Plain (IGP) during the period of 2016-2023. We have assessed the effectiveness of the Government of India's national mission, the National Clean Air Program (NCAP) in PM2.5 reduction over this city, and the study revealed that the mission has been efficacious in lessening the PM2.5 load by 28 % from pre-NCAP (2016-2019) to post-NCAP (2021-2023) periods. Several policy interventions reduced the contributions from various anthropogenic sources; however, biomass/solid waste burning remained a major concern with no significant reduction. The results revealed that the volume-weighted OP (OPv) remains mass-independent and the same when PM2.5 remains within 70 μg m-3 (OPv range between 2.7 and 3.1 nmol DTT min-1 m-3). With the rise in PM2.5 mass from 70 μg m-3, OPv boosts up sharply and reaches its peak (at ∼145 μg m-3 during pre-NCAP and ∼105 μg m-3 during post-NCAP) followed by an insignificant change with the further rise in PM2.5. We observed that biomass/solid waste burning is the major concern over Kolkata in the current scenario (post-NCAP) even after NCAP policy interventions. Such high OP-based toxicity of PM2.5 during post-NCAP periods could be minimized if actions are taken against this particular source.

Prenatal Exposure to Source-Specific Fine Particulate Matter and Autism Spectrum Disorder

In this study, associations between prenatal exposure to fine particulate matter (PM2.5) from 9 sources and development of autism spectrum disorder (ASD) were assessed in a population-based retrospective pregnancy cohort in southern California. The cohort included 318,750 mother-child singleton pairs. ASD cases (N = 4559) were identified by ICD codes. Source-specific PM2.5 concentrations were estimated from a chemical transport model with a 4 × 4 km2 resolution and assigned to maternal pregnancy residential addresses. Cox proportional hazard models were used to estimate the hazard ratios (HR) of ASD development for each individual source. We also adjusted for total PM2.5 mass and in a separate model for all other sources simultaneously. Increased ASD risk was observed with on-road gasoline (HR [CI]: 1.18 [1.13, 1.24]), off-road gasoline (1.15 [1.12, 1.19]), off-road diesel (1.08 [1.05, 1.10]), food cooking (1.05 [1.02, 1.08]), aircraft (1.04 [1.01, 1.06]), and natural gas combustion (1.09 [1.06, 1.11]), each scaled to standard deviation increases in concentration. On-road gasoline and off-road gasoline were robust for other pollutant groups. PM2.5 emitted from different sources may have different impacts on ASD. The results also identify PM source mixtures for toxicological investigations that may provide evidence for future public health policies.

Associations between Fine Particulate Matter Components, Their Sources, and Cognitive Outcomes in Children Ages 9-10 Years Old from the United States

BACKGROUND

Emerging literature suggests that fine particulate matter [with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 )] air pollution and its components are linked to various neurodevelopmental outcomes. However, few studies have evaluated how PM 2.5 component mixtures from distinct sources relate to cognitive outcomes in children.

OBJECTIVES

This cross-sectional study investigated how ambient concentrations of PM 2.5 component mixtures relate to neurocognitive performance in 9- to 10-year-old children, as well as explored potential source-specific effects of these associations, across the US.

METHODS

Using spatiotemporal hybrid models, annual concentrations of 15 chemical components of PM 2.5 were estimated based on the residential address of child participants from the Adolescent Brain Cognitive Development (ABCD) Study. General cognitive ability, executive function, and learning/memory scores were derived from the NIH Toolbox. We applied positive matrix factorization to identify six major PM 2.5 sources based on the 15 components, which included crustal, ammonium sulfate, biomass burning, traffic, ammonium nitrate, and industrial/residual fuel burning. We then utilized weighted quantile sum (WQS) and linear regression models to investigate associations between PM 2.5 components' mixture, their potential sources, and children's cognitive scores.

RESULTS

Mixture modeling revealed associations between cumulative exposure and worse cognitive performance across all three outcome domains, including shared overlap in detrimental effects driven by ammonium nitrates, silicon, and calcium. Using the identified six sources of exposure, source-specific negative associations were identified between ammonium nitrates and learning & memory, traffic and executive function, and crustal and industrial mixtures and general cognitive ability. Unexpected positive associations were also seen between traffic and general ability as well as biomass burning and executive function.

DISCUSSION

This work suggests nuanced associations between outdoor PM 2.5 exposure and childhood cognitive performance, including important differences in cognition related both to individual chemicals as well as to specific sources of these exposures. https://doi.org/10.1289/EHP14418.

Long-term source apportionment of PM2.5 across the contiguous United States (2000-2019) using a multilinear engine model

Identifying PM2.5 sources is crucial for effective air quality management and public health. This research used the Multilinear Engine (ME-2) model to analyze PM2.5 from 515 EPA Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE) sites across the U.S. from 2000 to 2019. The U.S. was divided into nine regions for detailed analysis. A total of seven source types (tracers) were resolved across the country: (1) Soil/Dust (Si, Al, Ca and Fe); (2) Vehicle emissions (EC, OC, Cu and Zn); (3) Biomass/wood burning (K); (4) Heavy oil/coal combustion (Ni, V, Cl and As); (5) Secondary sulfate (SO42-); (6) Secondary nitrate (NO3-) and (7) Sea salt (Mg, Na, Cl and SO42-). Furthermore, we extracted and calculated secondary organic aerosols (SOA) based on the secondary sulfate and nitrate factors. Notably, significant reductions in secondary sulfate, nitrate, and heavy oil/coal combustion emissions reflect recent cuts in fossil-fueled power sector emissions. A decline in SOA suggests effective mitigation of their formation conditions or precursors. Despite these improvements, vehicle emissions and biomass burning show no significant decrease, highlighting the need for focused control on these persistent pollution sources for future air quality management.

Application of artificial intelligence in quantifying lung deposition dose of black carbon in people with exposure to ambient combustion particles

BACKGROUND

Understanding lung deposition dose of black carbon is critical to fully reconcile epidemiological evidence of combustion particles induced health effects and inform the development of air quality metrics concerning black carbon. Macrophage carbon load (MaCL) is a novel cytology method that quantifies lung deposition dose of black carbon, however it has limited feasibility in large-scale epidemiological study due to the labor-intensive manual counting.

OBJECTIVE

To assess the association between MaCL and episodic elevation of combustion particles; to develop artificial intelligence based counting algorithm for MaCL assay.

METHODS

Sputum slides were collected during episodic elevation of ambient PM2.5 (n = 49, daily PM2.5 > 10 µg/m3 for over 2 weeks due to wildfire smoke intrusion in summer and local wood burning in winter) and low PM2.5 period (n = 39, 30-day average PM2.5 < 4 µg/m3) from the Lovelace Smokers cohort.

RESULTS

Over 98% individual carbon particles in macrophages had diameter <1 µm. MaCL levels scored manually were highly responsive to episodic elevation of ambient PM2.5 and also correlated with lung injury biomarker, plasma CC16. The association with CC16 became more robust when the assessment focused on macrophages with higher carbon load. A Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP) was developed based on the Mask Region-based Convolutional Neural Network. MacLEAP algorithm yielded excellent correlations with manual counting for number and area of the particles. The algorithm produced associations with ambient PM2.5 and plasma CC16 that were nearly identical in magnitude to those obtained through manual counting.

IMPACT STATEMENT

Understanding lung black carbon deposition is crucial for comprehending health effects of combustion particles. We developed "Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP)", the first artificial intelligence algorithm for quantifying airway macrophage black carbon. Our study bolstered the algorithm with more training images and its first use in air pollution epidemiology. We revealed macrophage carbon load as a sensitive biomarker for heightened ambient combustion particles due to wildfires and residential wood burning.

Long-term exposure to PM2.5 species and all-cause mortality among Medicare patients using mixtures analyses

BACKGROUND

The relationship between long-term exposure to PM2.5 and mortality is well-established; however, the role of individual species is less understood.

OBJECTIVES

In this study, we assess the overall effect of long-term exposure to PM2.5 as a mixture of species and identify the most harmful of those species while controlling for the others.

METHODS

We looked at changes in mortality among Medicare participants 65 years of age or older from 2000 to 2018 in response to changes in annual levels of 15 PM2.5 components, namely: organic carbon, elemental carbon, nickel, lead, zinc, sulfate, potassium, vanadium, nitrate, silicon, copper, iron, ammonium, calcium, and bromine. Data on exposure were derived from high-resolution, spatio-temporal models which were then aggregated to ZIP code. We used the rate of deaths in each ZIP code per year as the outcome of interest. Covariates included demographic, temperature, socioeconomic, and access-to-care variables. We used a mixtures approach, a weighted quantile sum, to analyze the joint effects of PM2.5 species on mortality. We further looked at the effects of the components when PM2.5 mass levels were at concentrations below 8 μg/m3, and effect modification by sex, race, Medicaid status, and Census division.

RESULTS

We found that for each decile increase in the levels of the PM2.5 mixture, the rate of all-cause mortality increased by 1.4% (95% CI: 1.3%-1.4%), the rate of cardiovascular mortality increased by 2.1% (95% CI: 2.0%-2.2%), and the rate of respiratory mortality increased by 1.7% (95% CI: 1.5%-1.9%). These effects estimates remained significant and slightly higher when we restricted to lower concentrations. The highest weights for harmful effects were due to organic carbon, nickel, zinc, sulfate, and vanadium.

CONCLUSIONS

Long-term exposure to PM2.5 species, as a mixture, increased the risk of all-cause, cardiovascular, and respiratory mortality.

Acidity and oxidative potential of atmospheric aerosols over a remote mangrove ecosystem during the advection of anthropogenic plumes

To investigate the acidity and the water-soluble oxidative potential of PM10, during the continental biomass-burning plume transport, a three-year (2018-2020) winter-time campaign was conducted over a pristine island (21.35°N, 88.32°E) of Sundarban mangrove ecosystem situated at the shore of Bay of Bengal. The average PM10 concentration over Sundarban was found to be 98.3 ± 22.2 μg m-3 for the entire study period with a high fraction of non-sea-salt- SO42- and water-soluble organic carbons (WSOC) that originated from the regional solid fuel burning. The thermodynamic E-AIM(IV) model had estimated that the winter-time aerosols over Sundarban were acidic (pH:2.4 ± 0.6) and mainly governed by non-sea-salt-SO42-. The volume and mass normalized oxidative potential of PM10 was found to be 1.81 ± 0.40 nmol DTT min-1 m-3 and 18.4 ± 6.1 pmol DTT min-1 μg-1 respectively which are surprisingly higher than several urban atmospheres across the world including IGP. The acid-digested water-soluble transition metals (Cu, Mn) show higher influences in the oxidative potential (under high aerosol acidity) compared to the WSOC. The study revealed that the advection of regional solid fuel burning plume and associated non-sea-salt-SO42- is enhancing aerosol acidity and oxidative stress that in turn alters the intrinsic properties of aerosols over such marine ecosystems rich in ecology and bio-geochemistry.

Long-term exposure to ambient PM2.5, particulate constituents and hospital admissions from non-respiratory infection

The association between PM2.5 and non-respiratory infections is unclear. Using data from Medicare beneficiaries and high-resolution datasets of PM2.5 and its constituents across 39,296 ZIP codes in the U.S between 2000 and 2016, we investigated the associations between annual PM2.5, PM2.5 constituents, source-specific PM2.5, and hospital admissions from non-respiratory infections. Each standard deviation (3.7-μg m-3) increase in PM2.5 was associated with a 10.8% (95%CI 10.8-11.2%) increase in rate of hospital admissions from non-respiratory infections. Sulfates (30.8%), Nickel (22.5%) and Copper (15.3%) contributed the largest weights in the observed associations. Each standard deviation increase in PM2.5 components sourced from oil combustion, coal burning, traffic, dirt, and regionally transported nitrates was associated with 14.5% (95%CI 7.6-21.8%), 18.2% (95%CI 7.2-30.2%), 20.6% (95%CI 5.6-37.9%), 8.9% (95%CI 0.3-18.4%) and 7.8% (95%CI 0.6-15.5%) increases in hospital admissions from non-respiratory infections. Our results suggested that non-respiratory infections are an under-appreciated health effect of PM2.5.

Acute exposure to total and source-specific ambient fine particulate matter and risk of respiratory disease hospitalization in Kuwait

Many epidemiologic studies concerned with acute exposure to ambient PM2.5 have reported positive associations for respiratory disease hospitalization. However, few studies have investigated this relationship in Kuwait and extrapolating results from other regions may involve considerable uncertainty due to variations in concentration levels, particle sources and composition, and population characteristics. Local studies can provide evidence for strategies to reduce risks from episodic exposures to high levels of ambient PM2.5 and generating hypotheses for evaluating health risks from chronic exposures. Therefore, using speciated PM2.5 data from local samplers, we analyzed the impact of daily total and source-specific PM2.5 exposure on respiratory hospitalizations in Kuwait using a case-crossover design with conditional quasi-Poisson regression. Total and source-specific ambient PM2.5 were modeled using 0-5-day cumulative distributed lags. For total PM2.5, we observed a 0.16% (95% confidence interval [CI] = 0.05, 0.27%) increase in risk for respiratory hospitalization per 1 μg/m3 increase in concentration. Of the source factors assessed, dust demonstrated a statistically significant increase in risk (0.16%, 95% CI = 0.04, 0.29%), and the central estimate for regional PM2.5 was positive (0.11%) but not statistically significant (95% CI = -0.11, 0.33%). No effect was observed from traffic emissions and 'other' source factors. When hospitalizations were stratified by sex, nationality, and age, we found that female, Kuwaiti national, and adult groups had higher effect estimates. These results suggest that exposure to ambient PM2.5 is harmful in Kuwait and provide some evidence of differential toxicity and effect modification depending on the PM2.5 source and population affected.

Pollution source identification of nitrogen and phosphorus in the lower West Main Canal, the Ganfu Plain irrigation district (South China)

The degradation of surface water quality has been a widespread concern around the world. However, irrigation canal water does not attract much attention although it is important to agriculture and population. In this study, a 5-year water quality monitoring of surface water was conducted in the lower West Main Canal of the Ganfu Plain irrigation district to identify the levels and pollution sources of nitrogen and phosphorus.Over 75% of samples had total phosphorus (TP) concentrations of > 0.02 mg/L, and all samples had total nitrogen (TN) concentrations of > 0.2 mg/L, indicating a risk of eutrophication. The concentrations of NO3--N and NH4+-N averagely occupied 57% and 18% of TN, respectively. PCA analysis showed that phosphorus and nitrogen in canal water were associated with meteorological factors, urban life and surface runoff, agricultural cultivation, livestock-poultry breeding, and water-sediment interaction in the wet season, whereas they were affected by meteorological factors, industrial effluent, urban domestic sewage, and livestock-poultry breeding in the dry season. Absolute principal component score-multiple linear regression (APCS-MLR) model results revealed that (1) agricultural cultivation plus livestock-poultry breeding contributed 43.2% of TP in canal water in the wet season, while livestock-poultry breeding contributed 52.9% in the dry season, and (2) domestic sewage plus surface runoff contributed 29.4% of TN in the wet season, while livestock-poultry breeding contributed 45.9% in the dry season. The unidentified sources had significant contributions of > 20% for almost all variables. So further investigations are required for determining unidentified sources, and anthropogenic pollution control is imperative for canal water quality protection.

Personal air pollution exposure and metals in the nasal epithelial lining fluid of COPD patients

Sampling of the nasal epithelial lining fluid is a potential method to assess exposure to air pollution within the respiratory tract among high risk populations. We investigated associations of short- and long-term particulate matter exposure (PM) and pollution-related metals in the nasal fluid of people with chronic obstructive pulmonary disease (COPD). This study included 20 participants with moderate-to-severe COPD from a larger study who measured long-term personal exposure to PM2.5 using portable air monitors and short-term PM2.5 and black carbon (BC) using in-home samplers for the seven days preceding nasal fluid collection. Nasal fluid was sampled from both nares by nasosorption, and inductively coupled plasma mass spectrometry was used to determine the concentration of metals with major airborne sources. Correlations of selected elements (Fe, Ba, Ni, Pb, V, Zn, Cu) were determined within the nasal fluid. Associations between personal long-term PM2.5 and seven day home PM2.5 and BC exposure and nasal fluid metal concentrations were determined by linear regression. Within nasal fluid samples, concentrations of vanadium and nickel (r = 0.8) and lead and zinc (r = 0.7) were correlated. Seven day and long-term PM2.5 exposure were both associated with higher levels of copper, lead, and vanadium in the nasal fluid. BC exposure was associated with higher levels of nickel in the nasal fluid. Levels of certain metals in the nasal fluid may serve as biomarkers of air pollution exposure in the upper respiratory tract.

Health risk assessment of PM2.5 and PM2.5-bound trace elements in Pretoria, South Africa

Exposure to outdoor air pollutants poses a risk for both non-carcinogenic and carcinogenic respiratory disease outcomes. A standardized health risk assessment (US EPA) utilizes air quality data, body mass and breathing rates to determine potential risk. This health risk assessment study assesses the hazard quotient (HQ) for total PM_2.5 and trace elemental constituents (Br, Cl, K, Ni, S, Si, Ti and U) exposure in Pretoria, South Africa. The World Health Organization (WHO) air quality guideline (5 µg m^-3) and the yearly South African National Ambient Air Quality Standard (NAAQS) (20 µg m^-3) were the references dosages for total PM_2.5. A total of 350 days was sampled in Pretoria, South Africa. The mean total PM_2.5 concentration during the 34-month study period was 23.2 µg m^-3 (0.7-139 µg m^-3). The HQ for total PM_2.5 was 1.17, 3.47 and 3.78 for adults, children and infants. Non-carcinogenic risks for trace elements K, Cl, S and Si were above 1 for adults. Seasonally, Si was the highest during autumn for adults (1.9) and during spring for S (5.5). The HQ values for K and Cl were highest during winter. The exposure to Ni posed a risk for cancer throughout the year and for As during winters.

Impact of traffic on air pollution in a mid-sized urban city during COVID-19 lockdowns

In this study, we evaluated the changes in air pollutant concentrations around Milwaukee, WI, during and after lockdown due to the COVID-19 pandemic for a period of 126 days. Measurements of particulate matter (PM1, PM2.5, and PM10), NH3, H2S, and O3 + NO2, were made on a 74-km route of arterial and highway roads from April to August 2020 using a Sniffer 4D sensor mounted to a vehicle. Traffic volume during measurement periods were estimated from smartphone-based traffic data. From lockdown (March 24, 2020-June 11, 2020) to post-lockdown (June 12, 2020-August 26, 2020) median traffic volume increased roughly 30-84%, depending upon the road type. In addition, increases in mean concentrations of NH3 (277%), PM (220-307%), and O3 + NO2 (28%) were also observed. For both traffic and air pollutants, abrupt changes in the data were observed mid-June, shortly after lockdown measures were lifted in Milwaukee County. Indeed, traffic was able to explain up to 57% of PM, 47% of NH3, and 42% of O3 + NO2 variance in pollutant concentrations on arterial and highway road segments. Two arterial roads that did not have statistically significant changes in traffic patterns during the lockdown exhibited no statistically significant trends between traffic and air quality parameters. This study demonstrated that COVID-19 lockdowns in Milwaukee, WI, caused significant decreases in traffic, which in turn had a direct impact on air pollutants. It also highlights the need for traffic volume and air quality data at relevant spatial and temporal scales for accurately assessing source apportionment of combustion-based air pollutants, which cannot be captured with typical ground-based sensor systems.

Contamination levels and source apportionment of potentially toxic elements in size-fractionated road dust of Moscow

The distributions of potentially toxic elements (PTEs) among PM₁, PM_1-10, PM_10-50, and PM_50-1000 fractions of the road dust were studied in the western and eastern parts of Moscow, impacted mainly by the road transport and the industrial sector, respectively. The partitioning of PTEs in road dust can provide more precise information on pollution sources and its further interpretation regarding human health risks. The concentrations of PTEs were analyzed by mass and atomic emission inductively coupled plasma spectrometry. Differences in the results between the western and eastern parts of the city were caused by the dissimilarity between traffic and industrial emissions. The source apportionment of the PTEs was carried out using absolute principal component analysis with multiple linear regressions (PCA/APCS-MLR). The contribution from anthropogenic sources was significant to PM₁ and PM_1-10 particles. In coarser fractions (PM_10-50, PM_50-1000), it decreased due to the input with the wind-induced resuspension of soil and rock particles. In the eastern part of the city, the accumulation of PTEs (especially Mo, Sb, Cd, Sn, Bi, Co, and As) is the most active in PM_1-10, while in the western part, it is most pronounced in PM₁ (especially Pb, Cu, Cr, and W) which is associated with differences in the size of particles coming from traffic and industrial sources. In the eastern part of Moscow, in comparison with the western part, the contribution from industrial sources to the accumulation of PTEs in all particle size fractions was higher by 10-30%. In the western part of Moscow, the finest particles PM₁ and PM_1-10 demonstrate the trend of rising pollution levels with the increase in road size, while in the eastern part of the city, only coarse particles PM_50-1000 show the same trend. In the fractions PM₁ and PM_1-10 of road dust, a significant contribution was made by anthropogenic sources; however, its role decreased in the coarse fractions-PM_10-50 and especially in PM_50-1000- due to the influence of roadside soils and their parent material.

Applying principal component pursuit to investigate the association between source-specific fine particulate matter and myocardial infarction hospitalizations in New York City

The association between fine particulate matter (PM_2.5) and cardiovascular outcomes is well established. To evaluate whether source-specific PM_2.5 is differentially associated with cardiovascular disease in New York City (NYC), we identified PM_2.5 sources and examined the association between source-specific PM_2.5 exposure and risk of hospitalization for myocardial infarction (MI).

Methods

We adapted principal component pursuit (PCP), a dimensionality-reduction technique previously used in computer vision, as a novel pattern recognition method for environmental mixtures to apportion speciated PM_2.5 to its sources. We used data from the NY Department of Health Statewide Planning and Research Cooperative System of daily city-wide counts of MI admissions (2007-2015). We examined associations between same-day, lag 1, and lag 2 source-specific PM_2.5 exposure and MI admissions in a time-series analysis, using a quasi-Poisson regression model adjusting for potential confounders.

Results

We identified four sources of PM_2.5 pollution: crustal, salt, traffic, and regional and detected three single-species factors: cadmium, chromium, and barium. In adjusted models, we observed a 0.40% (95% confidence interval [CI]: -0.21, 1.01%) increase in MI admission rates per 1 μg/m³ increase in traffic PM_2.5, a 0.44% (95% CI: -0.04, 0.93%) increase per 1 μg/m³ increase in crustal PM_2.5, and a 1.34% (95% CI: -0.46, 3.17%) increase per 1 μg/m³ increase in chromium-related PM_2.5, on average.

Conclusions

In our NYC study, we identified traffic, crustal dust, and chromium PM_2.5 as potentially relevant sources for cardiovascular disease. We also demonstrated the potential utility of PCP as a pattern recognition method for environmental mixtures.

Water quality modelling using principal component analysis and artificial neural network

The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R²) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.

Biochemical evidence of PM2.5 critical components for inducing myocardial fibrosis in vivo and in vitro

PM_2.5 constituents are tightly linked to the initiation of many cardiovascular diseases (CVDs). Little is known, however, about the events which critical components of PM_2.5 can induce the initiating events in CVDs. C57BL/6 female mice were exposed to PM_2.5 (3 mg/kg b.w.) from four different cities (Taiyuan, Beijing, Hangzhou, and Guangzhou) by oropharyngeal aspiration every other day. PM_2.5 from Taiyuan increased the diastolic function of the hearts and induced myocardial fibrosis with increased areas of interstitial fibrosis through the NOX4/TGF-β1/Smad 3/Col1a1 pathways. Pb, Cr, Mn, Zn, and most of the polycyclic aromatic hydrocarbons (PAHs) were positively associated with the related indicators of cardiac diastolic function and myocardial fibrosis by using Pearson correlation (R² = 0.9085-0.9897). To determine the critical components in PM_2.5 that can induce the occurrence of myocardial fibrosis, BEAS-2b cells were treated with one or more of five candidate components with/without Guangzhou PM_2.5, and then the conditioned medium of BEAS-2b was used to culture AC16 cells. The results showed that Zn + Pb + Mn + BaP with PM_2.5 from Guangzhou exposure significantly increased reactive oxygen species production of BEAS-2b cells and induced a dramatic increase of myocardial fiber-related gene expression (Col1a1 and TGF-β) in AC16 cells. It indicated that the different mass concentrations of Zn, Pb, Mn, and ΣPAHs in PM_2.5 might be the critical factors that modulated myocardial fibrosis induction by targeted. Our study provided a novel avenue for further elucidation of molecular mechanisms of PM_2.5 components-induced myocardial fibrosis.

Prenatal exposure to tailpipe and non-tailpipe tracers of particulate matter pollution and autism spectrum disorders

BACKGROUND

Traffic-related air pollution exposure is associated with increased risk of autism spectrum disorder (ASD). It is unknown whether carbonaceous material from vehicular tailpipe emissions or redox-active non-tailpipe metals, eg. from tire and brake wear, are responsible. We assessed ASD associations with fine particulate matter (PM_2.5) tracers of tailpipe (elemental carbon [EC] and organic carbon [OC]) and non-tailpipe (copper [Cu]; iron [Fe] and manganese [Mn]) sources during pregnancy in a large cohort.

METHODS

This retrospective cohort study included 318,750 children born in Kaiser Permanente Southern California (KPSC) hospitals during 2001-2014, followed until age 5. ASD cases were identified by ICD codes. Monthly estimates of PM_2.5 and PM_2.5 constituents EC, OC, Cu, Fe, and Mn with 4 km spatial resolution were obtained from a source-oriented chemical transport model. These exposures and NO₂ were assigned to each maternal address during pregnancy, and associations with ASD were assessed using Cox regression models adjusted for covariates. PM constituent effect estimates were adjusted for PM_2.5 and NO₂ to assess independent effects. To distinguish ASD risk associated with non-tailpipe from tailpipe sources, the associations with Cu, Fe, and Mn were adjusted for EC and OC, and vice versa.

RESULTS

There were 4559 children diagnosed with ASD. In single-pollutant models, increased ASD risk was associated with gestational exposures to tracers of both tailpipe and non-tailpipe emissions. The ASD hazard ratios (HRs) per inter-quartile increment of exposure) for EC, OC, Cu, Fe, and Mn were 1.11 (95% CI: 1.06-1.16), 1.09 (95% CI: 1.04-1.15), 1.09 (95% CI: 1.04-1.13), 1.14 (95% CI: 1.09-1.20), and 1.17 (95% CI: 1.12-1.22), respectively. Estimated effects of Cu, Fe, and Mn (reflecting non-tailpipe sources) were largely unchanged in two-pollutant models adjusting for PM_2.5, NO₂, EC or OC. In contrast, ASD associations with EC and OC were markedly attenuated by adjustment for non-tailpipe sources.

CONCLUSION

Results suggest that non-tailpipe emissions may contribute to ASD. Implications are that reducing tailpipe emissions, especially from vehicles with internal combustion engines, may not eliminate ASD associations with traffic-related air pollution.

Effects of shipwrecks on spatiotemporal dynamics of metal/loids in sediments and seafood safety in the Bay of Bengal

Metal/loid pollution from shipwrecking activities has drawn significant concern due to their persistent threat to the marine ecosystem and human health. We investigated the spatiotemporal distribution, pollution characteristics, risks, sources, and potential impact of metal/loids in the sediments and seafood in the Bay of Bengal at nearby open beaching shipwrecking yards in Bangladesh. We collected 78 sediments and 208 seafood samples from the exposed and control sites from 2018 to 2020 during the dry and wet seasons. The concentrations of 16 elements, including cadmium, arsenic, lead, chromium, manganese, copper, zinc, iron, tin, antimony, nickel, cobalt, molybdenum, vanadium, selenium, and thallium were measured using validated inductively coupled plasma-mass spectrometry (ICP-MS) methods. Based on the pollution indices (enrichment factor, geoaccumulation index, pollution index, and pollution load index), lead, arsenic, cadmium, selenium, copper, zinc, and tin from the dry season showed higher contaminations compared to the wet and their concentrations were increased from 2018 to 2020 with seasonal fluctuations. Sediment cadmium and arsenic posed relatively higher and moderate ecological risks. Health risk analysis indicated that lead, cadmium, and inorganic arsenic (estimated) in seafood species pose a possible health threat to the general population. Further, there were possible ecological and health risks for the metal/loids in combination based on the ecological risk index in sediment and the hazard index in seafood, respectively. Source apportionment suggested that anthropogenic activities through uncontrolled shipwrecking operations over the last four decades were the largest polluting dominator, contributing 55-77% of the metal/loid concentrations. Therefore, the data may inform mitigation strategies for emission control at the shipwrecking yards to protect marine ecosystems and their local population.

Fine particle components and risk of psychiatric hospitalization in the U.S

BACKGROUND

There is a lack of evidence for the associations between atmospheric particle components exposure and psychiatric health. We aimed to identify the most toxic particle component(s) and source(s) related with psychiatric illness.

METHODS

Using Health Cost and Utilization Project (HCUP) State Inpatient Databases (SIDs), we analyzed the relative risk (RR) of psychiatric hospitalization associated with increased residential exposure to 14 particle components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, sulfate (SO42-), nitrate (NO3-), organic carbon (OC), and elemental carbon (EC)). We covered the residents of eight U.S. states, who contributed to 5,012,041 psychiatric admissions over 2002-2018. Single component models were conducted via fitting zero-inflated negative binomial regression for each component with aggregated counts of total psychiatric hospitalizations per ZIP code per year as dependent variable. We used Nonnegative Matrix Factorization (NMF) to identify particle source factors and obtained the source-specific estimates. Generalized Weighted Quantile Sum (gWQS) Regression was applied to obtain an overall mixture effect. Separate but similar models were fitted for different age groups (<30 yrs. vs. ≥ 30 yrs) and psychiatric illness sub-categories to assess effect heterogeneity.

RESULTS

Sulfate, Fe, Pb and Zn were associated with the largest risk increases in single-component models. The biggest harmful associations were observed for metal industry source (high loadings of Pb and sulfate). For one quartile increase in components mixture score, we observed an adjusted RR of 1.24 (95 % CI, 1.21-1.26). Older population were more affected. We also observed higher increase in bipolar and psychotic admission risk for increased components source and mixture level.

CONCLUSION

Living in areas with higher levels of particle components was associated with increased risk of psychiatric hospitalization among the residents in eight U.S. states. Certain components (i.e. Pb, sulfate) and sources (metal industry) were the most related.

Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts: mixture analysis exploration

BACKGROUND

Numerous studies have documented PM_2.5's links with adverse health outcomes. Comparatively fewer studies have evaluated specific PM_2.5 components. The lack of exposure measurements and high correlation among different PM_2.5 components are two limitations.

METHODS

We applied a novel exposure prediction model to obtain annual Census tract-level concentrations of 15 PM_2.5 components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, SO₄^2-, NO₃^-, NH₄⁺, OC, EC) in Massachusetts from 2000 to 2015, to which we matched geocoded deaths. All non-accidental mortality, cardiovascular mortality, and respiratory mortality were examined for the population aged 18 or over. Weighted quantile sum (WQS) regression models were used to examine the cumulative associations between PM_2.5 components mixture and outcomes and each component's contributions to the cumulative associations. We have fit WQS models on 15 PM_2.5 components and a priori identified source groups (heavy fuel oil combustion, biomass burning, crustal matter, non-tailpipe traffic source, tailpipe traffic source, secondary particles from power plants, secondary particles from agriculture, unclear source) for the 15 PM_2.5 components. Total PM_2.5 mass analysis and single component associations were also conducted through quasi-Poisson regression models.

RESULTS

Positive cumulative associations between the components mixture and all three outcomes were observed from the WQS models. Components with large contribution to the cumulative associations included K, OC, and Fe. Biomass burning, traffic emissions, and secondary particles from power plants were identified as important source contributing to the cumulative associations. Mortality rate ratios for cardiovascular mortality were of greater magnitude than all non-accidental mortality and respiratory mortality, which is also observed in cumulative associations estimated from WQS, total PM_2.5 mass analysis, and single component associations.

CONCLUSION

We have found positive associations between the mixture of 15 PM_2.5 components and all non-accidental mortality, cardiovascular mortality, and respiratory mortality. Among these components, Fe, K, and OC have been identified as having important contribution to the cumulative associations. The WQS results also suggests potential source effects from biomass burning, traffic emissions, and secondary particles from power plants.

Real-time measurements of mineral dust concentration in coarse particulate matter (PM10-2.5) by employing a novel optical-based technique in Los Angeles

As a primary component of coarse particulate matter (PM), ambient mineral dust has been linked to adverse health effects. Los Angeles, the largest metropolitan urban area of the United States, is impacted by both windblown and localized sources of mineral dust, often internally mixed with black carbon. The estimation of mineral dust concentrations with a high time resolution becomes critical in improving our understanding of its sources and temporal trends. Using Aethalometers combined with a high-volume virtual impactor (VI) to enrich coarse (2.5 <dp < 10 μm) particles, the light absorption and mass concentration of mineral dust were estimated in real-time during summer, fall, and winter over 2020-2021. The concentration-enriched coarse PM was collected on Teflon filters, and its chemical composition in terms of trace elements and metals was chemically quantified. The high time-resolution measurements enabled us to calculate the absorption coefficient of enriched dust particles by subtracting the light absorption of the post-VI coarse PM from that of the PM_2.5 aerosol fraction to reduce the impact of stronger light absorbers in ambient PM. Mineral dust was more prevalent during the fall and winter campaigns (i.e., 19.3 and 11.4 μg/m³, respectively), lower concentrations were observed during the summer campaign (i.e., 8.50 μg/m³). The calculated absorption Ångström exponent (AAE) was 2.18, highlighting the presence of dust particles during the sampling period. The dust mass absorption coefficient was estimated to be 2.7 ± 1.6 Mm^-1 at 370 nm and 0.41 ± 0.16 Mm^-1 at 880 nm wavelengths, respectively. The validation of the proposed approach was investigated by comparing the evaluated mineral dust mass concentrations in this study with the reported coarse PM concentrations by the California Air Resources Board (CARB). The results reported by the optical-based approach with high temporal resolution can provide crucial information on identifying sources of mineral dust in urban areas.

The importance of mineralogical composition for the cytotoxic and pro-inflammatory effects of mineral dust

Background

Respirable mineral particles represent a potential health hazard in occupational settings and ambient air. Previous studies show that mineral particles may induce cytotoxicity and inflammatory reactions in vitro and in vivo and that the potency varies between samples of different composition. However, the reason for these differences is largely unknown and the impact of mineralogical composition on the biological effects of mineral dust remains to be determined.

Methods

We have assessed the cytotoxic and pro-inflammatory effects of ten mineral particle samples of different composition in human bronchial epithelial cells (HBEC3-KT) and THP-1-derived macrophages, as well as their membranolytic properties in erythrocytes. Moreover, the results were compiled with the results of recently published experiments on the effects of stone particle exposure and analysed using linear regression models to elucidate which mineral components contribute most to the toxicity of mineral dust.

Results

While all mineral particle samples were more cytotoxic to HBEC3-KT cells than THP-1 macrophages, biotite and quartz were among the most cytotoxic in both cell models. In HBEC3-KT cells, biotite and quartz also appeared to be the most potent inducers of pro-inflammatory cytokines, while the quartz, Ca-feldspar, Na-feldspar and biotite samples were the most potent in THP-1 macrophages. All particle samples except quartz induced low levels of membranolysis. The regression analyses revealed associations between particle bioactivity and the content of quartz, muscovite, plagioclase, biotite, anorthite, albite, microcline, calcite, chlorite, orthopyroxene, actinolite and epidote, depending on the cell model and endpoint. However, muscovite was the only mineral consistently associated with increased cytotoxicity and cytokine release in both cell models.

Conclusions

The present study provides further evidence that mineral particles may induce cytotoxicity and inflammation in cells of the human airways and that particle samples of different mineralogical composition differ in potency. The results show that quartz, while being among the most potent samples, does not fully predict the toxicity of mineral dust, highlighting the importance of other particle constituents. Moreover, the results indicate that the phyllosilicates muscovite and biotite may be more potent than other minerals assessed in the study, suggesting that this group of sheet-like minerals may warrant further attention.

Long-Term Exposure to Source-Specific Fine Particles and Mortality─A Pooled Analysis of 14 European Cohorts within the ELAPSE Project

We assessed mortality risks associated with source-specific fine particles (PM2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM2.5 components was assessed at baseline residential addresses. The source apportionment resulted in the identification of five sources: traffic, residual oil combustion, soil, biomass and agriculture, and industry. In single-source analysis, all identified sources were significantly positively associated with increased natural mortality risks. In multisource analysis, associations with all sources attenuated but remained statistically significant with traffic, oil, and biomass and agriculture. The highest association per interquartile increase was observed for the traffic component (HR: 1.06; 95% CI: 1.04 and 1.08 per 2.86 μg/m3 increase) across five identified sources. On a 1 μg/m3 basis, the residual oil-related PM2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM2.5 mass, suggesting that past estimates using the generic PM2.5 exposure response function have underestimated the potential clean air health benefits of reducing fossil-fuel combustion. Source-specific associations with cause-specific mortality were in general consistent with findings of natural mortality.

Mortality Attributable to Long-Term Exposure to Ambient Fine Particulate Matter: Insights from the Epidemiologic Evidence for Understudied Locations

Epidemiologic cohort studies have consistently demonstrated that long-term exposure to ambient fine particles (PM_2.5) is associated with mortality. Nevertheless, extrapolating results to understudied locations may involve considerable uncertainty. To explore this issue, this review discusses the evidence for (i) the associated risk of mortality, (ii) the shape of the concentration-response function, (iii) a causal interpretation, and (iv) how the source mix/composition of PM_2.5 and population characteristics may alter the effect. The accumulated evidence suggests the following: (i) In the United States, the change in all-cause mortality risk per μg/m³ is about 0.8%. (ii) The concentration-response function appears nonlinear. (iii) Causation is overwhelmingly supported. (iv) Fossil fuel combustion-related sources are likely more toxic than others, and age, race, and income may modify the effect. To illustrate the use of our findings in support of a risk assessment in an understudied setting, we consider Kuwait. However, given the complexity of this relationship and the heterogeneity in reported effects, it is unreasonable to think that, in such circumstances, point estimates can be meaningful. Consequently, quantitative probabilistic estimates, which cannot be derived objectively, become essential. Formally elicited expert judgment can provide such estimates, and this review provides the evidence to support an elicitation.

Revealing the Chemical Profiles of Airborne Particulate Matter Sources in Lake Baikal Area: A Combination of Three Techniques

Positive matrix factorization (PMF) is a widely used multivariate source apportionment technique. However, PMF-derived source profiles are never compared to real ones because of the absence of data on the chemical composition of source emissions. The aim of this study was to verify the validity of PMF-derived source profiles using the diagnostic ratios (DR) method and end-member mixing analysis (EMMA). The composition of polycyclic aromatic hydrocarbons (PAHs) in particulate matter (PM) sampled in the air above Lake Baikal in summer and the composition of inorganic elements (IE) in PM accumulated in Lake Baikal snowpack were used as study objects. Five PAH sources and five IE sources were identified using PMF. Eight PAHs and six IEs selected from PMF-derived source profiles were recognized as eligible for calculating the DRs (species 1/(species 1 + species 2)) suitable for testing PMF results using EMMA. EMMA was based on determining whether most samples in mixing diagrams that use DR values as coordinates of source points could be bound by a geometrical shape whose vertices are pollution sources. It was found that the four PAH sources and four IE sources obtained using PMF were also identified using EMMA. Thus, the validity of the most of PMF-derived source profiles was proved.

Chemometric approach in environmental pollution analysis: A critical review

With the ever-increasing global population and industrialization, it has become a call of the hour to start taking care of the environment to balance the ecosystem. For this, effective monitoring and assessment are required, which involves collecting and measuring environmental details, temporal and spatial readings of environmental data, and parameters. However, assessment of the environment is very tedious as it includes monitoring target analytes, identifying their sources, and reporting, which invariably implies that detailed environmental monitoring would be an intricate and expensive process. The traditional protocols in environmental measures are often manual and time demanding, which makes it further difficult. Moreover, several changes also occur within the environment, which could be chemical, physical, or biological, and since these environmental impacts are often cumulative, it becomes difficult to measure an isolated system. Furthermore, the chances of skipping significant results and trends become high. Also, experimental data obtained from the environmental analysis are usually non-linear and multi-variant due to different associations among various contributing variables. Therefore, it is implied that accurate measurements and environment monitoring are not using traditional analytical protocols. Thus, the need for a chemometric approach in environmental pollution analysis becomes paramount due to the inherent limitations associated with the conventional approach of analyzing environmental datasets. Chemometrics has appeared as a potential technique, which enhances the particulars of the chemical datasets by using statistical and mathematical analysis methods to analyze chemical data beyond univariate analysis. Utilizing chemometrics to study the environmental data is a revolutionary idea as it helps identify the relationship between sources of contaminations, environmental drivers, and their impact on the environment. Hence, this review critically explores the concept of chemometrics and its application in environmental pollution analysis by briefly highlighting the idea of chemometrics, its types, applications, advantages, and limitations in the environmental domain. An attempt is also made to present future trends in applications of chemometrics in environmental pollution analysis.

A hybrid satellite and land use regression model of source-specific PM2.5 and PM2.5 constituents

Although PM_2.5 mass varies in source and composition over time and space, most health effects assessment have made the inherent assumption that all PM_2.5 mass has the same health implications, irrespective of composition. Nationwide estimates of source-specific PM_2.5 mass and constituents at local-scale would allow for epidemiological studies and health effects assessments that consider the variability in PM_2.5 characteristics in their health impact assessments. In response, we developed US models of annual exposures at the census tract level for five major PM_2.5 sources (traffic, soil, coal, oil, and biomass combustion) and six trace elements (elemental carbon, sulfur, silicon, selenium, nickel, and non-soil potassium) for 2001 through 2014. We employed Absolute Factor Analysis (APCA) to derive the source-specific PM_2.5 impacts at monitoring stations. Random forest algorithms that incorporated predictors derived from satellite, chemical transport model, and census tract resolution land-use data on traffic, meteorology, and emissions, which were rigorously tested by 10-fold cross-validation (CV), were then employed to estimate elemental and source-specific PM_2.5 levels at non-monitoring site census-tracts over the study years. Model performances were moderate to good, with CV R² ranging from 0.41 to 0.95. For PM_2.5 sources, the highest CV R² was attained for traffic PM_2.5 (CV R² = 0.73), followed by coal (CV R² = 0.65), oil (CV R² = 0.62), soil (CV R² = 0.60), and biomass (CV R² = 0.41). Among constituents, the CV was highest for sulfur (CV R² = 0.95). Our analyses provided highly resolved spatial estimates of annual elemental and source-specific PM_2.5 concentrations at the census-tract level, for 2001 through 2014. This dataset offers exposure estimates in support of future nationwide long-term health effects studies of source-specific PM_2.5 mass and constituents, enabling epidemiological research that addresses the fact that not all particles are the same.

Vehicular Traffic in Urban Areas: Health Burden and Influence of Sustainable Urban Planning and Mobility

Vehicular traffic is one of the major sources of air pollution in European cities. This work aims to understand which characteristics of the urban environment could influence mobility-related air pollution, quantify the health impacts of exposure to traffic-derived PM2.5 and NO2 concentrations, and assess the potential health benefits expected from traffic interventions. The health benefits modeled are intended to provide a set of comparable data to support decision-makers and encourage informed decision-making to design healthier cities. Targeting a large geographical coverage, 12 European cities from 9 countries were comparatively assessed in terms of mean daily traffic volume/area, the number of public transport stops/area, and the percentage of green and outdoor leisure areas, among other urban indicators. This was implemented using an open-source data mining tool, which was seen as a useful engine to identify potential strategies to improve air quality. The comparison of urban indicators in the selected cities evidenced two trends: (a) cities with the most heterogeneous distribution of public transport stops, as an indicator of poor accessibility, are also those with the lowest proportion of km dedicated to cycleways and footways, highlighting the need in these cities for more sustainable mobility management; and (b) the percentage of green and outdoor leisure areas may influence the share of journeys by bicycle, pointing out that promoting the perception of green routes is relevant to enhance the potential of active transport modes. Socioeconomic factors can be key determinants of the urban indicators and would need further consideration. For the health impact assessment (HIA), two baseline scenarios were evaluated and compared. One is based on mean annual traffic contributions to PM2.5 concentrations in each target city (ranging between 1.9 and 13 µg/m3), obtained from the literature, and the second is grounded on mean annual NO2 concentrations at all available traffic and urban background stations within each city (17.2–83.5 µg/m3), obtained from the European Environment Agency database. The intervention scenarios modeled were designed based on traffic mitigation strategies in the literature, and set to ranges of 6–50% in traffic-derived PM2.5 concentrations and of 4–12.5% in NO2 concentrations. These scenarios could result in only a 1.7% (0.6–4%) reduction in premature mortality due to exposure to traffic-derived PM2.5, and 1.0% (0.4–2%) due to exposure to NO2, as the mean for all the cities. This suggests that more ambitious pollution abatement strategies should be targeted.

The Impact of COVID-19 Related Changes on Air Quality in Birmingham, Alabama, United States

Air pollution is responsible for a wide range of health effects in exposed populations. Variations in local air pollution can affect local population health outcomes. The strict regulations imposed during the peak of the COVID-19 pandemic ('lockdowns') resulted in a unique situation where human mobility was limited significantly, resulting in improved air quality in several major cities. The main goal of this study was to investigate if lockdowns during the COVID-19 pandemic significantly impacted air quality in Birmingham, Alabama-a city with a history of high air pollution levels-with a focus on PM2.5 (Particulate Matter with an aerodynamic diameter ≤2.5 µm) and NO2 (Nitrogen dioxide). Daily air pollutant and traffic data were obtained for the Birmingham Metropolitan Area for the period January to October 2020, and previous years. Mean PM2.5 and NO2 concentrations and traffic volumes during the official city/state lockdown period (24 March to 30 April 2020) were compared to pre- and post-lockdown means. The mean PM2.5 and NO2 concentrations during the lockdown did not significantly differ from that of the pre- or post-lockdown periods. However, NO2 significantly decreased even after the lockdown order was removed, with the mean decreasing significantly compared to pre-lockdown and lockdown periods. Both PM2.5 and NO2 annual means in 2020 were significantly lower than the annual means in 2019, indicating the occurrence of significant changes over the longer term that were not limited by defined lockdown periods. Traffic significantly increased after the lockdown order was removed but did not correlate with the two pollutants studied. Therefore, we conclude that the Stay at Home/lockdown regulations and other COVID-19 restrictions had an impact on the air quality of Birmingham Alabama; although these lockdown impacts varied for each pollutant and were not limited only by the official lockdown dates/periods.

The pro-inflammatory effects of combined exposure to diesel exhaust particles and mineral particles in human bronchial epithelial cells

BACKGROUND

People are exposed to ambient particulate matter (PM) from multiple sources simultaneously in both environmental and occupational settings. However, combinatory effects of particles from different sources have received little attention in experimental studies. In the present study, the pro-inflammatory effects of combined exposure to diesel exhaust particles (DEP) and mineral particles, two common PM constituents, were explored in human lung epithelial cells.

METHODS

Particle-induced secretion of pro-inflammatory cytokines (CXCL8 and IL-1β) and changes in expression of genes related to inflammation (CXCL8, IL-1α, IL-1β and COX-2), redox responses (HO-1) and xenobiotic metabolism (CYP1A1 and CYP1B1) were assessed in human bronchial epithelial cells (HBEC3-KT) after combined exposure to different samples of DEP and mineral particles. Combined exposure was also conducted using lipophilic organic extracts of DEP to assess the contribution of soluble organic chemicals. Moreover, the role of the aryl hydrocarbon receptor (AhR) pathway was assessed using an AhR-specific inhibitor (CH223191).

RESULTS

Combined exposure to DEP and mineral particles induced increases in pro-inflammatory cytokines and expression of genes related to inflammation and redox responses in HBEC3-KT cells that were greater than either particle sample alone. Moreover, robust increases in the expression of CYP1A1 and CYP1B1 were observed. The effects were most pronounced after combined exposure to α-quartz and DEP from an older fossil diesel, but enhanced responses were also observed using DEP generated from a modern biodiesel blend and several stone particle samples of mixed mineral composition. Moreover, the effect of combined exposure on cytokine secretion could also be induced by lipophilic organic extracts of DEP. Pre-incubation with an AhR-specific inhibitor reduced the particle-induced cytokine responses, suggesting that the effects were at least partially dependent on AhR.

CONCLUSIONS

Exposure to DEP and mineral particles in combination induces enhanced pro-inflammatory responses in human bronchial epithelial cells compared with exposure to the individual particle samples. The effects are partly mediated through an AhR-dependent pathway and lipophilic organic chemicals in DEP appear to play a central role. These possible combinatory effects between different sources and components of PM warrant further attention and should also be considered when assessing measures to reduce PM-induced health effects.

The impact of Long-Term PM2.5 constituents and their sources on specific causes of death in a US Medicare cohort

BACKGROUND

Our understanding of the impact of long-term exposures to PM_2.5 constituents and sources on mortality is limited.

OBJECTIVES

To examine associations between long-term exposures to PM_2.5 constituents and sources and cause-specific mortality in US older adults.

METHODS

We obtained demographic and mortality data for 15.4 million Medicare beneficiaries living within the conterminous United States (US) between 2000 and 2008. We assessed PM_2.5 constituents exposures for each beneficiary and used factor analysis and residual-based methods to characterize PM_2.5 sources and mixtures, respectively. In age-, sex-, race- and site- stratified Cox proportional hazard models adjusted for neighborhood socio-economic status (SES), we assessed associations of individual PM_2.5 constituents, sources, and mixtures and cause-specific mortality and examined modification of these associations by participant demographics and location of residence. We assessed the robustness of our findings to additional adjustment for behavioral risk factors and to alternate exposure definitions and exposure windows.

RESULTS

Hazard ratios (HR) were highest for all causes of death, except COPD, for PM_2.5 constituents and the coal combustion-related PM_2.5 components, with no evidence of confounding by behavioral covariates. We further found Pb and metal-related PM_2.5 components to be significantly associated with increased HR of all causes of death, except COPD and lung cancer mortality, and nitrate (NO₃^-) and silicon (Si) and associated source-related PM_2.5 components (traffic and soil, respectively) to be significantly associated with increased all-cause, CVD, respiratory and all cancer-related mortality HR. Associations for other examined constituents and mortality were inconsistent or largely null. Our analyses of mixtures were generally consistent with these findings. Mortality HRs were greatest for minority, especially Black, low-income urban, younger, and male beneficiaries.

DISCUSSION

PM_2.5 components related to coal combustion, traffic, and to a lesser extent, soil were strongly associated with mortality from CVD, respiratory disease, and cancer.

Association of abnormal-glucose tolerance during pregnancy with exposure to PM2.5 components and sources

Maternal exposure to PM_2.5 has been associated with abnormal glucose tolerance during pregnancy, but little is known about which constituents and sources are most relevant to glycemic effects. We conducted a retrospective cohort study of 1148 pregnant women to investigate associations of PM_2.5 chemical components with gestational diabetes mellitus (GDM) and impaired glucose tolerance (IGT) and to identify the most harmful sources in Heshan, China from January 2015 to July 2016. We measured PM_2.5 using filter-based method and analyzed them for 28 constituents, including carbonaceous species, water-soluble ions and metal elements. Contributions of PM_2.5 sources were assessed by positive matrix factorization (PMF). Logistic regression model was used to estimate composition-specific and source-specific effects on GDM/IGT. Random forest algorithm was applied to evaluate the relative importance of components to GDM and IGT. PM_2.5 total mass and several chemical constituents were associated with GDM and IGT across the early to mid-gestation periods, as were the PM_2.5 sources fossil fuel/oil combustion, road dust, metal smelting, construction dust, electronic waster, vehicular emissions and industrial emissions. The trimester-specific associations differed among pollutants and sources. The third and highest quartile of elemental carbon, ammonium (NH₄⁺), iron (Fe) and manganese (Mn) across gestation were consistently associated with higher odds of GDM/IGT. Maternal exposures to zinc (Zn), titanium (Ti) and vehicular emissions during the first trimester, and vanadium (V), nickel (Ni), road dust and fossil fuel/oil combustion during the second trimester were more important for GDM/IGT. This study provides important new evidence that maternal exposure to PM_2.5 components and sources is significantly related to elevated risk for abnormal glucose tolerance during pregnancy.

Source Apportionment of Particulate Matter in Urban Snowpack Using End-Member Mixing Analysis and Positive Matrix Factorization Model

The aim of this study was to identify particulate matter (PM) sources and to evaluate their contributions to PM in the snowpack of three East Siberian cities. That was the first time when the PM accumulated in the snowpack during the winter was used as the object for source apportionment study in urban environment. The use of long-term integrated PM samples allowed to exclude the influence of short-term weather conditions and anthropogenic activities on PM chemistry. To ascertain the real number of PM sources and their contributions to air pollution the results of source apportionment using positive matrix factorization model (PMF) were for the first time compared to the results obtained using end-member mixing analysis (EMMA). It was found that Si, Fe and Ca were the tracers of aluminosilicates, non-exhaust traffic emissions and concrete deterioration respectively. Aluminum was found to be the tracer of both fossil fuel combustion and aluminum production. The results obtained using EMMA were in good agreement with those obtained using PMF. However, in some cases, the non-point sources identified using PMF were the combinations of two single non-point sources identified using EMMA, whereas the non-point sources identified using EMMA were split by PMF into two single non-point sources. The point sources were clearly identified using both techniques.

Atmospheric deposition of biologically relevant trace metals in the eastern Adriatic coastal area

Aerosol (PM₁₀), bulk deposition, sea surface microlayer (SML) and underlying water (ULW) samples were collected simultaneously during a field campaign at the middle Adriatic coastal site between February and July 2019, to assess the impact of atmospheric deposition (AD) of biologically relevant trace metals (TM) (Zn, Cu, Co, Ni, Cd and Pb) on the sea surface responses in an oligotrophic coastal region. Anthropogenic emissions from continental Europe, alongside local/regional domestic heating, likely affected the concentrations of Zn, Cd and Pb in aerosols during winter-early spring, while traffic emissions during the tourist season impacted Ni, Co and Cu aerosol concentrations. Additionally, open-fire biomass burning (BB) episodes caused considerable TM concentration increases, while Saharan dust intrusion in spring led to a 10-fold increase in Co concentrations in PM₁₀ samples. These intensive episodes significantly affected the bulk deposition fluxes of TMs, showing that a small number of such extreme events, common to Mediterranean coastal areas, could be responsible for most of the AD. Enrichments and concentrations of total TMs in SML samples collected following BB events indicated that such events, along with high precipitation, influenced TM partitioning in surface water layers. We estimated that AD represents a significant source of TM to the shallow middle Adriatic coastal area, highlighting the need to further explore the atmosphere-sea surface links, to expand our understanding of the biogeochemistry of these important micronutrients and pollutants, including their impact on the aquatic community.

Non-nutritive suck and airborne metal exposures among Puerto Rican infants

Air pollution has been shown to impact multiple measures of neurodevelopment in young children. Its effects on particularly vulnerable populations, such as ethnic minorities, however, is less studied. To address this gap in the literature, we assess the associations between infant non-nutritive suck (NNS), an early indicator of central nervous system integrity, and air pollution exposures in Puerto Rico. Among infants aged 0-3 months enrolled in the Center for Research on Early Childhood Exposure and Development (CRECE) cohort from 2017 to 2019, we examined associations between exposure to fine particulate matter (PM_2.5) and its components on infant NNS in Puerto Rico. NNS was assessed using a pacifier attached to a pressure transducer, allowing for real-time visualization of NNS amplitude, frequency, duration, cycles/burst, cycles/min and bursts/min. These data were linked to 9-month average prenatal concentrations of PM_2.5 and components, measured at three community monitoring sites. We used linear regression to examine the PM_2.5-NNS association in single pollutant models, controlling for infant sex, maternal age, gestational age, and season of birth in base and additionally for household smoke exposure, age at testing, and NNS duration in full models. Among 198 infants, the average NNS amplitude and burst duration was 17.1 cmH₂O and 6.1 s, respectively. Decreased NNS amplitude was consistently and significantly associated with 9-month average exposure to sulfur (-1.026 ± 0.507), zinc (-1.091 ± 0.503), copper (-1.096 ± 0.535) vanadium (-1.157 ± 0.537), and nickel (-1.530 ± 0.501). Decrements in NNS frequency were associated with sulfur exposure (0.036 ± 0.018), but not other examined PM components. Our findings provide new evidence that prenatal maternal exposure to specific PM components are associated with impaired neurodevelopment in Puerto Rican infants soon after birth.

The Role of Fossil Fuel Combustion Metals in PM2.5 Air Pollution Health Associations

In this review, we elucidate the central role played by fossil fuel combustion in the health-related effects that have been associated with inhalation of ambient fine particulate matter (PM2.5). We especially focus on individual properties and concentrations of metals commonly found in PM air pollution, as well as their sources and their adverse health effects, based on both epidemiologic and toxicological evidence. It is known that transition metals, such as Ni, V, Fe, and Cu, are highly capable of participating in redox reactions that produce oxidative stress. Therefore, particles that are enriched, per unit mass, in these metals, such as those from fossil fuel combustion, can have greater potential to produce health effects than other ambient particulate matter. Moreover, fossil fuel combustion particles also contain varying amounts of sulfur, and the acidic nature of the resulting sulfur compounds in particulate matter (e.g., as ammonium sulfate, ammonium bisulfate, or sulfuric acid) makes transition metals in particles more bioavailable, greatly enhancing the potential of fossil fuel combustion PM2.5 to cause oxidative stress and systemic health effects in the human body. In general, there is a need to further recognize particulate matter air pollution mass as a complex source-driven mixture, in order to more effectively quantify and regulate particle air pollution exposure health risks.

Travelling to polluted cities: a systematic review on the harm of air pollution on international travellers' health

RATIONALE FOR REVIEW

In 2019, approximately, 1.4 billion people travelled internationally. Many individuals travel to megacities where air pollution concentrations can vary significantly. Short-term exposure to air pollutants can cause morbidity and mortality related to cardiovascular and respiratory disease, with the literature clearly reporting a strong association between short-term exposure to particulate matter ≤2.5 μm and ozone with adverse health outcomes in resident populations. However, limited research has been conducted on the health impacts of short-term exposure to air pollution in individuals who travel internationally. The objective of this systematic review was to review the evidence for the respiratory and cardiovascular health impacts from exposure to air pollution during international travel to polluted cities in adults aged ≥18 years old.

KEY FINDINGS

We searched PubMed, Scopus and EMBASE for studies related to air pollution and the health impacts on international travellers. Of the initially identified 115 articles that fit the search criteria, 6 articles were selected for the final review. All six studies found indications of adverse health impacts of air pollution exposure on international travellers, with most of the changes being reversible upon return to their home country/city. However, none of these studies contained large populations nor investigated vulnerable populations, such as children, elderly or those with pre-existing conditions.

CONCLUSIONS

More research is warranted to clearly understand the impacts of air pollution related changes on travellers' health, especially on vulnerable groups who may be at higher risk of adverse impacts during travel to polluted cities.

Heart Failure and PAHs, OHPAHs, and Trace Elements Levels in Human Serum: Results from a Preliminary Pilot Study in Greek Population and the Possible Impact of Air Pollution

Cardiovascular diseases (CVDs) have been associated with environmental pollutants. The scope of this study is to assess any potential relation of polycyclic aromatic hydrocarbons (PAHs), their hydroxylated derivatives, and trace elements with heart failure via their direct determination in human serum of Greek citizens residing in different areas. Therefore, we analyzed 131 samples including cases (heart failure patients) and controls (healthy donors), and the respective demographic data were collected. Significantly higher concentrations (p < 0.05) were observed in cases' serum regarding most of the examined PAHs and their derivatives with phenanthrene, fluorene, and fluoranthene being the most abundant (median of >50 μg L-1). Among the examined trace elements, As, Cd, Cu, Hg, Ni, and Pb were measured at statistically higher concentrations (p < 0.05) in cases' samples, with only Cr being significantly higher in controls. The potential impact of environmental factors such as smoking and area of residence has been evaluated. Specific PAHs and trace elements could be possibly related with heart failure development. Atmospheric degradation and smoking habit appeared to have a significant impact on the analytes' serum concentrations. PCA-logistic regression analysis could possibly reveal common mechanisms among the analytes enhancing the hypothesis that they may pose a significant risk for CVD development.

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.

Cardiovascular morbidity and mortality associations with biomass- and fossil-fuel-combustion fine-particulate-matter exposures in Dhaka, Bangladesh

BACKGROUND

Fine-particulate-matter (i.e. with an aerodynamic diameter of ≤2.5 µm, PM2.5) air pollution is commonly treated as if it had 'equivalent toxicity', irrespective of the source and composition. We investigate the respective roles of fossil-fuel- and biomass-combustion particles in the PM2.5 relationship with cardiovascular morbidity and mortality using tracers of sources in Dhaka, Bangladesh. Results provide insight into the often observed levelling of the PM2.5 exposure-response curve at high-pollution levels.

METHODS

A time-series regression model, adjusted for potentially confounding influences, was applied to 340 758 cardiovascular disease (CVD) emergency-department visits (EDVs) during January 2014 to December 2017, 253 407 hospital admissions during September 2013 to December 2017 and 16 858 CVD deaths during January 2014 to October 2017.

RESULTS

Significant associations were confirmed between PM2.5-mass exposures and increased risk of cardiovascular EDV [0.27%, (0.07% to 0.47%)] at lag-0, hospitalizations [0.32% (0.08% to 0.55%)] at lag-0 and deaths [0.87%, (0.27% to 1.47%)] at lag-1 per 10-μg/m3 increase in PM2.5. However, the relationship of PM2.5 with morbidity and mortality effect slopes was less steep and non-significant at higher PM2.5 concentrations (during crop-burning-dominated exposures) and varied with PM2.5 source. Fossil-fuel-combustion PM2.5 had roughly a four times greater effect on CVD mortality and double the effect on CVD hospital admissions on a per-µg/m3 basis than did biomass-combustion PM2.5.

CONCLUSION

Biomass burning was responsible for most PM2.5 air pollution in Dhaka, but fossil-fuel-combustion PM2.5 dominated the CVD adverse health impacts. Such by-source variations in the health impacts of PM2.5 should be considered in conducting ambient particulate-matter risk assessments, as well as in prioritizing air-pollution-mitigation measures and clinical advice.

Atmospheric particle-bound polycyclic aromatic hydrocarbons, n-alkanes, hopanes, steranes and trace metals: PM2.5 source identification, individual and cumulative multi-pathway lifetime cancer risk assessment in the urban environment

The occurrence of atmospheric fine particles (PM_2.5)-associated polycyclic aromatic hydrocarbons (PAHs), trace metals and organic molecular markers was investigated by conducting an intensive sampling campaign at the Eastern Mediterranean urban area of Nicosia (Cyprus). Sixty-two 24-hr PM_2.5 samples were collected and analyzed for fifty parent and alkylated PAHs, twenty-five long chain n-alkanes, seventeen hopanes and twelve steranes used for source apportionment. The same number and kind of samples were analyzed to determine twenty-eight trace metals. Emphasis was given to investigate the air levels of the scarcely monitored although highly carcinogenic PAHs such as dibenzopyrenes, dibenzoanthracenes, 7H-benzo[c]fluorene and 5-methyl-chrysene, not included in the USEPA's sixteen PAH priority list (USEPA-16). UNMIX receptor model was applied to apportion the sources of atmospheric emissions of the determined organic compounds and trace metals and evaluate their daily contributions to the corresponding PM_2.5 associated concentrations. For comparison purposes, principal component analysis with multiple linear regression (PCA/MLR) was also applied and its results are reported. The UNMIX receptor model, compared to PCA/MLR, offered a more precise source profile and more reliable daily mass source distributions by eliminating negative contributions. The individual and cumulative multi-pathway lifetime cancer risk (posed via inhalation, ingestion and dermal contact) by exposure to PM_2.5-associated USEPA-16 listed and non-listed PAHs and selected airborne trace metals (As, Cd, Co, Ni, and Pb) were assessed. To estimate the contribution of each emission source to the total cancer risk, multiple linear regression analysis was performed, using as independent variables the daily source mass contributions and as dependent variables the respective cancer risk units. The estimated total cumulative cancer risk comprising all toxic PAHs, besides those included in the priority list, and metals was higher than the USEPA's threshold by a factor of eight, denoting a potential risk for long-term exposure of a population in the urban environment.

A case-crossover study of short-term air pollution exposure and the risk of stillbirth in California, 1999-2009

BACKGROUND

Associations between ambient air pollution and stillbirth have recently been explored, but most studies have focused on long-term (trimester or gestational averages) rather than short-term (within one week) air pollution exposures.

OBJECTIVE

To evaluate whether short-term exposures to criteria air pollutants are associated with increased risk of stillbirth.

METHODS

Using air pollution and fetal death certificate data from 1999 to 2009, we assessed associations between acute prenatal air pollution exposure and stillbirth in California. In a time-stratified case-crossover study, we analyzed single day and/or cumulative average days (up to a 6 day lag) of exposure to fine (PM_2.5) and coarse particles (PM_10-2.5), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO) for mothers estimated to reside within 10 km of a pollution monitor based on reported zip code. We also examined potential confounding by apparent temperature or co-pollutants, and effect modification by maternal demographic factors, fetal sex, gestational age, and cause of stillbirth.

RESULTS

Stillbirth cases in the primary analyses ranged between 1,203 and 13,018, depending on the pollutant. For an IQR increase in SO₂ (lag 4), O₃ (lag 4), and PM_10-2.5 (lag 2), we found a 2.8% (95% confidence interval (CI) 0.2%, 5.5%), 5.8% (95% CI 1.6%, 10.1%), and 6.1% (95% CI 0.1%, 12.4%) increase in the odds of stillbirth, respectively. Additional adjustment by apparent temperature had little effect on the SO₂ association but slightly attenuated O₃ (adjusted % change: 4.2% (95% CI -0.2%, 8.9%) and PM_10-2.5 (5.7% (95% CI -1.1%, 13.0%)) associations, while other co-pollutants had minimal impact. Associations were observed specifically for stillbirths from obstetric complications and in women with higher educational attainment.

CONCLUSIONS

This study provides evidence for associations between prenatal short-term air pollution exposure, specifically SO₂, O_3, and PM_10-2.5, and stillbirth in California and warrants replication of findings in other settings.

The Respiratory Risks of Ambient/Outdoor Air Pollution

Globally, exposure to ambient air pollutants is responsible for premature mortality and is implicated in the development and exacerbation of several acute and chronic lung disease across all ages. In this article, we discuss the source apportionment of ambient pollutants and the respiratory health effects in humans. We specifically discuss the evidence supporting ambient pollution in the development of asthma and chronic obstructive pulmonary disease and acute exacerbations of each condition. Practical advice is given to health care providers in how to promote a healthy environment and advise patients with chronic conditions to avoid unsafe air quality.

Spatial variation and source apportionment of surface water pollution in the Tuo River, China, using multivariate statistical techniques

The increasingly serious water pollution of rivers has attracted wide attention from all countries in the world. Investigating spatial variations of water pollution and source apportionment is particularly important for the effective management of river quality. The water samples collected every two months at 31 sampling sites containing 12 water quality parameters during 2018 and 2019 were analyzed to investigate the spatial patterns and the apportionment of the pollutants in the Tuo River. Cluster analysis (CA), pollution index (PI), factor analysis (FA), principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR) were used in the current study. The PI found that the Tuo River was most severely polluted with phosphorus and nitrogen. Additionally, compared with that in 2018, the water quality in the Tuo River has significantly improved in 2019. The CA divided the sampling sites into three categories, which are defined as clean, low-polluted, and moderate-polluted areas, respectively. FA/PCA resulted in four latent pollution sources, explaining 74.09% of the total variance. The contributions of the identified pollution sources to pollutants were realized using APCS-MLR. Most variables were mainly affected by the pollution of agricultural runoff, industrial wastewater, domestic sewage, and soil weathering. According to the results, we can also find that agricultural runoff and industrial wastewater were dominating in the Tuo River. These results provide a scientific basis for formulating more reasonable and strict pollution control strategies for the Tuo River.

Chemometrics for environmental monitoring: a review

Environmental monitoring is necessary to ensure the overall health and conservation of an ecosystem. However, ecosystems (e.g. air, water, soil), are complex, involving numerous processes (both native and external), inputs, contaminants, and living organisms. As such, monitoring an environmental system is not a trivial task. The data obtained from natural systems is often multifaceted and convoluted, as a multitude of inputs can be intertwined within the matrix of the information obtained as part of a study. This means that trends and important results can be easily overlooked by conventional and single dimensional data analysis protocols. Recently, chemometric methods have emerged as a powerful method for maximizing the details contained within a chemical data set. Specifically, chemometrics refers to the use of mathematical and statistical analysis methods to evaluate chemical data, beyond univariant analysis. This type of analysis can provide a quantitative description of environmental measurements, while also having the capacity to reveal previously overlooked trends in data sets. Applying chemometrics to environmental data allows us to identify and describe the inter-relationship of environmental drivers, sources of contamination, and their potential impact upon the environment. This review aims to provide a detailed understanding of chemometric techniques, how they are currently used in environmental monitoring, and how these techniques can be used to improve current practices. An enhanced ability to monitor environmental conditions and to predict trends would be greatly beneficial to government and research agencies in their ability to develop environmental policies and analytical procedures.