Long-term source apportionment of ambient fine particulate matter (PM2.5) in the Los Angeles Basin: a focus on emissions reduction from vehicular sources

Review of health effects driven by aerosol acidity: Occurrence and implications for air pollution control

Acidity, generally expressed as pH, plays a crucial role in atmospheric processes and ecosystem evolution. Atmospheric acidic aerosol, triggering severe air pollution in the industrialization process (e.g., London Great Smoke in 1952), has detrimental effects on human health. Despite global endeavors to mitigate air pollution, the variation of aerosol acidity remains unclear and further restricts the knowledge of the acidity-driven toxicity of fine particles (PM2.5) in the atmosphere. Here, we summarize the toxicological effects and mechanisms of inhalable acidic aerosol and its response to air pollution control. The acidity could adjust toxic components (e.g., metals, quinones, and organic peroxides) bonded in aerosol and synergize with oxidant gaseous pollutants (e.g., O3 and NO2) in epithelial lining fluid to induce oxidative stress and inflammation. The inhaled aerosol from the ambient air with higher acidity might elevate airway responsiveness and cause worse pulmonary dysfunction. Furthermore, historical observation data and model simulation indicate that PM2.5 can retain its acidic property despite considerable reductions in acidifying gaseous pollutants (e.g., SO2 and NOx) from anthropogenic emissions, suggesting its continuing adverse impacts on human health. The study highlights that aerosol acidity could partially offset the health benefits of emission reduction, indicating that acidity-related health effects should be considered for future air pollution control policies.

Sources of personal PM2.5 exposure during pregnancy in the MADRES cohort

BACKGROUND

Personal exposure to fine particulate matter (PM2.5) is impacted by different sources each with different chemical composition. Determining these sources is important for reducing personal exposure and its health risks especially during pregnancy.

OBJECTIVE

Identify main sources and their contributions to the personal PM2.5 exposure in 213 women in the 3rd trimester of pregnancy in Los Angeles, CA.

METHODS

We measured 48-hr integrated personal PM2.5 exposure and analyzed filters for PM2.5 mass, elemental composition, and optical carbon fractions. We used the EPA Positive Matrix Factorization (PMF) model to resolve and quantify the major sources of personal PM2.5 exposure. We then investigated bivariate relationships between sources, time-activity patterns, and environmental exposures in activity spaces and residential neighborhoods to further understand sources.

RESULTS

Mean personal PM2.5 mass concentration was 22.3 (SD = 16.6) μg/m3. Twenty-five species and PM2.5 mass were used in PMF with a final R2 of 0.48. We identified six sources (with major species in profiles and % contribution to PM2.5 mass) as follows: secondhand smoking (SHS) (brown carbon, environmental tobacco smoke; 65.3%), fuel oil (nickel, vanadium; 11.7%), crustal (aluminum, calcium, silicon; 11.5%), fresh sea salt (sodium, chlorine; 4.7%), aged sea salt (sodium, magnesium, sulfur; 4.3%), and traffic (black carbon, zinc; 2.6%). SHS was significantly greater in apartments compared to houses. Crustal source was correlated with more occupants in the household. Aged sea salt increased with temperature and outdoor ozone, while fresh sea salt was highest on days with westerly winds from the Pacific Ocean. Traffic was positively correlated with ambient NO2 and traffic-related NOx at residence. Overall, 76.8% of personal PM2.5 mass came from indoor or personal compared to outdoor sources.

IMPACT

We conducted source apportionment of personal PM2.5 samples in pregnancy in Los Angeles, CA. Among identified sources, secondhand smoking contributed the most to the personal exposure. In addition, traffic, crustal, fuel oil, fresh and aged sea salt sources were also identified as main sources. Traffic sources contained markers of combustion and non-exhaust wear emissions. Crustal source was correlated with more occupants in the household. Aged sea salt source increased with temperature and outdoor ozone and fresh sea salt source was highest on days with westerly winds from the Pacific Ocean.

Effects of seasonal management programs on PM2.5 in Seoul and Beijing using DN-PMF: Collaborative efforts from the Korea-China joint research

South Korea and China have implemented increasingly stringent mitigation measures to reduce the health risks from PM2.5 exposure, jointly conducting a ground-based air quality observation study in Northeast Asia. Dispersion normalized positive matrix factorization (DN-PMF) was used to identify PM2.5 sources in Seoul and Beijing and assess the effectiveness of the seasonal management programs (SMPs) through a comparative study. Samples were collected during three periods: January-December 2019, September 2020-May 2021, and July 2021-March 2022. In Seoul, ten sources were resolved (Secondary nitrate: 8.67 μg/m3, 34 %, Secondary sulfate: 5.67 μg/m3, 22 %, Motor vehicle: 1.83 μg/m3, 7.2 %, Biomass burning: 2.30 μg/m3, 9.1 %, Residual oil combustion: 1.66 μg/m3, 6.5 %, Industry: 2.15 μg/m3, 8.5 %, Incinerator: 1.39 μg/m3, 5.5 %, Coal combustion: 0.363 μg/m3, 1.4 %, Road dust/soil: 0.941 μg/m3, 3.7 %, Aged sea salt: 0.356 μg/m3, 1.4 %). The SMP significantly decreased PM2.5 mass concentrations and source contributions of motor vehicle, residual oil combustion, industry, coal combustion, and biomass burning sources (p-value < 0.05). For Seoul, the reduction effects of the SMPs were evident even considering the influence of the natural meteorological variations and the responses to COVID-19. In Beijing, nine sources were resolved (Secondary nitrate: 12.6 μg/m3, 28 %, Sulfate: 8.27 μg/m3, 18 %, Motor vehicle: 3.77 μg/m3, 8.4 %, Biomass burning: 2.70 μg/m3, 6.0 %, Incinerator: 4.50 μg/m3, 10 %, Coal combustion: 3.52 μg/m3, 7.8 %, Industry: 5.01 μg/m3, 11 %, Road dust/soil: 2.92 μg/m3, 6.5 %, Aged sea salt: 1.63 μg/m3, 3.6 %). Significant reductions in PM2.5 mass concentrations and source contributions of industry, coal combustion, and incinerator (p-value < 0.05) were observed, attributed to the SMP and additional measures enforced before the 2022 Beijing Winter Olympics. Unlike comparing PM2.5 mass concentration variations using conventional methods, investigation of the source contribution variations of PM2.5 by using DN-PMF can provide a deeper understanding of the effectiveness of the air quality management policies.

Exploration and comparison of the relationship between PAHs and ROS in PM2.5 emitted from multiple anthropogenic sources in the Guanzhong Plain, China

Anthropogenic emissions have emerged as an important source of urban atmospheric PM2.5, exacerbating air pollution and the associated health implications. This study analyses PM2.5, originating from major anthropogenic sources (industries, motor vehicles, and solid-fuel combustion for domestic applications) in the Guanzhong Plain in China, along with the parent- (p-), alkylated- (a-), and oxygenated- (o-) polycyclic aromatic hydrocarbons (PAHs) and reactive oxygen species (ROS) levels in PM2.5. Industrial emissions are mainly characterised by high abundances of benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), and benz[a]fluoranthene (BaF). The 4-ring p-PAHs, such as fluoranthene (FLA), pyrene (PYR), benzo[a]anthracene (BaA), and chrysene (CHR) proportions and the diagnostic ratios of indeno[1,2,3-cd]pyrene (IcdP)/[IcdP + benzo[ghi]perylene (BghiP)] and 1-acenaphthenone (1ACO)/[1ACO + 9-fluorenone (9FO)] in motor vehicle emission PM2.5 were higher than the other sources. Household solid fuel combustion features high proportions of methylnaphthalene (M-NAP), i.e., 2 M-NAP and 1 M-NAP and 3-ring p-PAHs. Acenaphthylene (ACY), acenaphthene (ACE), anthracene (ANT), 1,4-chrysenequinone (1,4CHRQ), and reactive oxygen species (ROS) were positively correlated among the three anthropogenic sources. Moreover, the correlations between other PAHs and ROS varied significantly among the three sources. As mixed and compound organic pollutants, 2- and 3-ring p-PAHs were more positively correlated with the ROS activity of household solid fuel combustion sources compared with industrial and motor vehicle sources. Based on the relative contribution of these three sources to PAHs in PM2.5, we estimated the cancer risks of males and females in the Guanzhong area to be 2.95 × 10-6 and 2.87 × 10-6, respectively, exceeding the safety threshold of 1 × 10-6. This study provides a basic dataset for conducting a refined source apportionment of PM2.5 and a scientific basis for further understanding the relationship between PM2.5, PAHs, and ROS in northern China.

Assessing the Oxidative Potential of Outdoor PM2.5 in Wintertime Fairbanks, Alaska

The oxidative potential (OP) of outdoor PM2.5 in wintertime Fairbanks, Alaska, is investigated and compared to those in wintertime Atlanta and Los Angeles. Approximately 40 filter samples collected in January-February 2022 at a Fairbanks residential site were analyzed for OP utilizing dithiothreitol-depletion (OPDTT) and hydroxyl-generation (OPOH) assays. The study-average PM2.5 mass concentration was 12.8 μg/m3, with a 1 h average maximum of 89.0 μg/m3. Regression analysis, correlations with source tracers, and contrast between cold and warmer events indicated that OPDTT was mainly sensitive to copper, elemental carbon, and organic aerosol from residential wood burning, and OPOH to iron and organic aerosol from vehicles. Despite low photochemically-driven oxidation rates, the water-soluble fraction of OPDTT was unusually high at 77%, mainly from wood burning emissions. In contrast to other locations, the Fairbanks average PM2.5 mass concentration was higher than Atlanta and Los Angeles, whereas OPDTT in Fairbanks and Atlanta were similar, and Los Angeles had the highest OPDTT and OPOH. Site differences were observed in OP when normalized by both the volume of air sampled and the particle mass concentration, corresponding to exposure and the intrinsic health-related properties of PM2.5, respectively. The sensitivity of OP assays to specific aerosol components and sources can provide insights beyond the PM2.5 mass concentration when assessing air quality.

Los Angeles Basin's air quality transformation: a long-term investigation on the impacts of PM regulations on the trends of ultrafine particles and co-pollutants

This study investigates the long-term trends of ambient ultrafine particles (UFPs) and associated airborne pollutants in the Los Angeles Basin from 2007 to 2022, focusing on the indirect effects of regulations on UFP levels. The particle number concentration (PNC) of UFPs was compiled from previous studies in the area, and associated co-pollutant data, including nitrogen oxides (NOx), carbon monoxide (CO), elemental carbon (EC), organic carbon (OC), and ozone (O3), were obtained from the chemical speciation network (CSN) database. Over the study period, a general decrease was noted in the PNC of UFPs, NOx, EC, and OC, except for CO, the concentration trends of which did not exhibit a consistent pattern. UFPs, NOx, EC, and OC were positively correlated, while O3 had a negative correlation, especially with NOx. Our analysis discerned two distinct subperiods in pollutant trends: 2007-2015 and 2016-2022. For example, there was an overall decrease in the PNC of UFPs at an annual rate of -850.09 particles/cm3/year. This rate was more pronounced during the first sub-period (2007-2015) at -1814.9 particles/cm3/year and then slowed to -227.21 particles/cm3/year in the second sub-period (2016-2023). The first sub-period (2007-2015) significantly influenced pollutant level changes, exhibiting more pronounced and statistically significant changes than the second sub-period (2016-2022). Since 2016, almost all primary pollutants have stabilized, indicating a reduced impact of current regulations, and emphasizing the need for stricter standards. In addition, the study included an analysis of Vehicle Miles Traveled (VMT) trends from 2007 to 2022 within the Los Angeles Basin. Despite the general increase in VMT, current regulations and cleaner technologies seem to have successfully mitigated the potential increase in increase in PNC. Overall, while a decline in UFPs and co-pollutant levels was observed, the apparent stabilization of these levels underscores the need for more stringent regulatory measures and advanced emission standards.

Contributions of non-tailpipe emissions to near-road PM2.5 and PM10: A chemical mass balance study

As the importance of non-tailpipe particles (NTP) over tailpipe emissions from urban traffic has been increasing, there is a need to evaluate NTP contributions to ambient particulate matter (PM) using representative source profiles. The Brake and Tire Wear Study conducted in Los Angeles, California in the winter of 2020 collected 64 PM2.5 and 64 PM10 samples from 32 pairs of downwind-upwind measurements at two near-road locations (I-5 in Anaheim and I-710 in Long Beach). These samples were characterized for inorganic and organic markers and, along with locally-developed brake wear, tire wear, and road dust source profiles, subject to source apportionment using the effective-variance chemical mass balance (EV-CMB) model. Model results highlighted the dominance of resuspended dust in both PM2.5 (23-33%) and PM10 (32-53%). Brake and tire wear contributed more to PM2.5 than tailpipe exhausts (diesel + gasoline) for I-5 (29-30% vs. 19-21%) while they were comparable for I-710 (15-17% vs. 15-19%). For PM10, the brake and tire wear contributions were 2-3 times the exhaust contributions. Different fleet compositions on and near I-5 and I-710 appeared to influence the relative importance of NTP and exhaust sources. The downwind-upwind differences in source contributions were often insignificant, consistent with small and/or nearly equal impacts of adjacent highway traffic emissions on the downwind and upwind sites. The utility of sole markers, such as barium and zinc, to predict brake and tire wear abundances in ambient PM is evaluated.

Size-segregated source identification of water-soluble and water-insoluble metals and trace elements of coarse and fine PM in central Los Angeles

In this study, the water-solubility and sources of metals and trace elements in both fine and coarse particulate matter (PM) were investigated in Central Los Angeles. Sampling was performed in the winter, spring, and summer of 2022 at the Particle Instrumentation Unit (PIU) of the University of Southern California located in the proximity of I-110 freeway. Both fine and coarse PM samples were collected using Personal Cascade Impactors (PCIS) and chemically analyzed to determine their water-soluble and water-insoluble metal content. Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) were used to determine the sources of soluble and insoluble metals and obtain their contributions to total metal concentration. Our results indicate that the water-solubility of most of the metals is higher in the fine size fraction compared to the coarse fraction. Seasonal variations in the water solubility of selected metals for both coarse and fine fractions were observed, with higher water-soluble metal concentrations in summer for several species (e.g., Fe , S, Pb, Cu, La, Ni, and Al ), possibly due to higher photochemical processing, while in winter, almost all species exhibited higher insoluble fraction concentrations. The PCA and MLR analyses results showed that tire and brake wear was the most significant contributor to the total metals for both fine soluble and insoluble portions, accounting for 35% and 75% of the total metals, respectively. Combustion sources also contributed substantially to water-soluble metals for fine and coarse size ranges, representing 40% and 32% of the total metal mass, respectively. In addition, mineral dust and soil and re-suspended dust were identified as the highest contributors to coarse metals. The MLR analysis also revealed that secondary aerosols contributed 11% to the fine water-soluble metals. Our results suggest that non-tailpipe emissions significantly contribute to both coarse and fine PM metals in the Central Los Angeles region.

Assessing Lifetime Cancer Risk Associated with Population Exposure to PM-Bound PAHs and Carcinogenic Metals in Three Mid-Latitude Metropolitan Cities

Lifetime cancer risk characterization of ambient PM-bound carcinogenic metals and polycyclic aromatic hydrocarbons (PAHs) were examined in the cities of Los Angeles (USA), Thessaloniki (Greece) and Milan (Italy), which share similar Mediterranean climates but are different in their urban emission sources and governing air quality regulations. The samples in Milan and Thessaloniki were mostly dominated by biomass burning activities whereas the particles collected in Los Angeles were primary impacted by traffic emissions. We analyzed the ambient PM2.5 mass concentration of Cadmium (Cd), Hexavalent Chromium (Cr(VI)), Nickel (Ni), Lead (Pb), as well as 13 PAH compounds in the PM samples, collected during both cold and warm periods at each location. Pb exhibited the highest annual average concentration in all three cities, followed by Ni, As, Cr(VI), Cd and PAHs, respectively. The cancer risk assessment based on outdoor pollutants was performed based on three different scenarios, with each scenario corresponding to a different level of infiltration of outdoor pollutants into the indoor environment. Thessaloniki exhibited a high risk associated with lifetime inhalation of As, Cr(VI), and PAHs, with values in the range of (0.97-1.57) × 10-6, (1.80-2.91) × 10-6, and (0.77-1.25) × 10-6, respectively. The highest cancer risk values were calculated in Milan, exceeding the US EPA standard by a considerable margin, where the lifetime risk values of exposure to As, Cr(VI), and PAHs were in the range of (1.29-2.08) × 10-6, (6.08-9.82) × 10-6, and (1.10-1.77) × 10-6, respectively. In contrast, the estimated risks associated with PAHs and metals, except Cr(VI), in Los Angeles were extremely lower than the guideline value, even when the infiltration factor was assumed to be at peak. The lifetime cancer risk values associated with As, Cd, Ni, Pb, and PAHs in Los Angeles were in the range of (0.04-0.33) × 10-6. This observation highlights the impact of local air quality measures in improving the air quality and lowering the cancer risks in Los Angeles compared to the other two cities.

Drive less but exposed more? Exploring social injustice in vehicular air pollution exposure

Despite growing understanding of racial and class injustice in vehicular air pollution exposure, less is known about the relationship between people's exposure to vehicular air pollution and their contribution to it. Taking Los Angeles as a case study, this study examines the injustice in vehicular PM_2.5 exposure by developing an indicator that measures local populations' vehicular PM_2.5 exposure adjusted by their vehicle trip distances. This study applies random forest regression models to assess how travel behavior, demographic, and socioeconomic characteristics affect this indicator. The results indicate that census tracts of the periphery whose residents drive longer distances are exposed to less vehicular PM_2.5 pollution than tracts in the city center whose residents drive shorter distances. Ethnic minority and low-income tracts emit little vehicular PM_2.5 and are particularly exposed to it, while White and high-income tracts generate more vehicular PM_2.5 pollution but are less exposed.

Particulate matter exposure and chronic cerebral hypoperfusion promote oxidative stress and induce neuronal and oligodendrocyte apoptosis in male mice

Chronic cerebral hypoperfusion (CCH) may amplify the neurotoxicity of nanoscale particulate matter (nPM), resulting in white matter injury. This study characterized the joint effects of nPM (diameter ≤ 200 nm) and CCH secondary to bilateral carotid artery stenosis (BCAS) exposure on neuronal and white matter injury in a murine model. nPM was collected near a highway and re-aerosolized for exposure. Ten-week-old C57BL/6 male mice were randomized into four groups: filtered air (FA), nPM, FA + BCAS, and nPM + BCAS. Mice were exposed to FA or nPM for 10 weeks. BCAS surgeries were performed. Markers of inflammation, oxidative stress, and apoptosis were examined. nPM + BCAS exposure increased brain hemisphere TNFα protein compared to FA. iNOS and HNE immunofluorescence were increased in the corpus callosum and cerebral cortex of nPM + BCAS mice compared to FA. While nPM exposure alone did not decrease cortical neuronal cell count, nPM decreased corpus callosum oligodendrocyte cell count. nPM exposure decreased mature oligodendrocyte cell count and increased oligodendrocyte precursor cell count in the corpus callosum. nPM + BCAS mice exhibited a 200% increase in cortical neuronal TUNEL staining and a 700% increase in corpus callosum oligodendrocyte TUNEL staining compared to FA. There was a supra-additive interaction between nPM and BCAS on cortical neuronal TUNEL staining (2.6× the additive effects of nPM + BCAS). nPM + BCAS exposure increased apoptosis, neuroinflammation, and oxidative stress in the cerebral cortex and corpus callosum. nPM + BCAS exposure increased neuronal apoptosis above the separate responses to each exposure. However, oligodendrocytes in the corpus callosum demonstrated a greater susceptibility to the combined neurotoxic effects of nPM + BCAS exposure.

Evidence of non-tailpipe emission contributions to PM2.5 and PM10 near southern California highways

Particulate Matter (PM) concentrations near highways are influenced by vehicle tailpipe and non-tailpipe emissions, other emission sources, and urban background aerosols. This study collected PM_2.5 and PM₁₀ filter samples near two southern California highways (I-5 and I-710) over two weeks in winter 2020. Samples were analyzed for chemical source markers. Mean PM_2.5 and PM₁₀ concentrations were approximately 10-15 and 30 μg/m³, respectively. Organic matter, mineral dust, and elemental carbon (EC) were the most abundant PM components. EC and polycyclic aromatic hydrocarbons at I-710 were 19-26% and 47% higher than those at the I-5 sites, respectively, likely due to a larger proportion of diesel vehicles. High correlations were found for elements with common sources, such as markers for brake wear (e.g., Fe, Ba, Cu, and Zr) and road dust (e.g., Al, Si, Ca, and Mn). Based on rubber abundances, the contributions of tire tread particles to PM_2.5 and PM₁₀ mass were approximately 8.0% at I-5 and 5.5% at I-710. Two different tire brands showed significantly different Si, Zn, carbon, and natural rubber abundances.

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.

Ambient PM2.5 and O3 pollution and health impacts in Iranian megacity

The main objectives of this study were to (i) assess variation within fine particles (PM_2.5) and tropospheric ozone (O₃) time series in Khorramabad (Iran) between 2019 (before) and 2020 (during COVID-19 pandemic); (ii) assess relationship between PM_2.5 and O₃, the PM_2.5/O₃ ratio, and energy consumption; and (iii) estimate the health effects of exposure to ambient PM_2.5 and O₃. From hourly PM_2.5 and O₃ concentrations, we applied both linear-log and integrated exposure-response functions, city-specific relative risk, and baseline incidence values to estimate the health effects over time. A significant correlation was found between PM_2.5 and O₃ (r =-0.46 in 2019, r =-0.55 in 2020, p < 0.05). The number of premature deaths for all non-accidental causes (27.5 and 24.6), ischemic heart disease (7.3 and 6.3), chronic obstructive pulmonary disease (17 and 19.2), and lung cancer (9.2 and 6.25) attributed to ambient PM_2.5 exposure and for respiratory diseases (4.7 and 5.4) for exposure to O₃ above 10 µg m^-3 for people older than 30-year-old were obtained in 2019 and 2020. The number of years of life lost declined by 11.6% in 2020 and exposure to PM_2.5 reduced the life expectancy by 0.58 and 0.45 years, respectively in 2019 and 2020. Compared to 2019, the restrictive measures associated to COVID-19 pandemic led to reduction in PM_2.5 (-25.5%) and an increase of O₃ concentration (+ 8.0%) in Khorramabad.

Investigation of organic carbon profiles and sources of coarse PM in Los Angeles

Source apportionment analyses are essential tools to determine sources of ambient coarse particles (2.5 <d_p < 10 μm) and to disentangle their association and contribution from other pollutants, particularly PM_2.5 (<2.5 μm). A semi-continuous sampling campaign was conducted using two virtual impactors/concentrators to enhance coarse particulate matter concentrations coupled with an online thermal-optical EC/OC monitor to quantify coarse PM-bound organic carbon volatility fractions (OC₁-OC₄) in central Los Angeles during the winter, spring, and summer of 2021. The total OC and its volatility fraction concentrations, meteorological parameters (i.e., wind speeds and relative humidity), vehicle miles traveled (VMT), and gaseous source tracers (i.e., O₃ and NO₂) were used as inputs to positive matrix factorization (PMF) model. A 3-factor solution identified vehicular emissions (accounting for 46% in the cold phase and 26% in the warm phase of total coarse OC concentrations), secondary organic carbon (27% and 37%), and re-suspended dust (27% and 37%) as the primary organic carbon sources of coarse PM. The re-suspended dust factor showed a higher contribution of more volatile organic carbons (i.e., OC₁ up to 77%) due to their re-distribution on dust particles, whereas the SOA factor was the dominant contributor to less volatile organic aerosols (i.e., OC₄ up to 54%), which are the product of reactions at high relative humidity (RH). Our findings revealed that the total OC concentrations in the coarse size range were comparable with those of previous studies in the area, underscoring the challenges in curtailing coarse PM-bound OC sources and the necessity of developing effective emission control regulations on coarse PM. The results from the current study provide insights into the seasonal and temporal variation of total OC and its volatility fractions in Los Angeles.

Unequal airborne exposure to toxic metals associated with race, ethnicity, and segregation in the USA

Persons of color have been exposed to a disproportionate burden of air pollution across the United States for decades. Yet, the inequality in exposure to known toxic elements of air pollution is unclear. Here, we find that populations living in racially segregated communities are exposed to a form of fine particulate matter with over three times higher mass proportions of known toxic and carcinogenic metals. While concentrations of total fine particulate matter are two times higher in racially segregated communities, concentrations of metals from anthropogenic sources are nearly ten times higher. Populations living in racially segregated communities have been disproportionately exposed to these environmental stressors throughout the past decade. We find evidence, however, that these disproportionate exposures may be abated though targeted regulatory action. For example, recent regulations on marine fuel oil not only reduced vanadium concentrations in coastal cities, but also sharply lessened differences in vanadium exposure by segregation.

Tailpipe and Nontailpipe Emission Factors and Source Contributions of PM10 on Major Freeways in the Los Angeles Basin

In this study, the emission factors of PM₁₀ and its chemical constituents from various contributing sources including nontailpipe and tailpipe emissions were estimated on two interstate freeways in the Los Angeles basin. PM₁₀ samples were collected on the I-110 and I-710 freeways as well as at the University of Southern California (USC) campus as the urban background site, while freeway and urban background CO₂ levels were measured simultaneously. PM₁₀ samples were analyzed for their content of chemical species which were used to estimate the emission factors of PM₁₀ and its constituents on both I-110 and I-710 freeways. The estimated values were employed to determine the emission factors for light (LDV) and heavy-duty vehicles (HDV). The quantified species were also processed by the positive matrix factorization (PMF) model to produce PM₁₀ freeway source profiles and their contribution to PM₁₀ mass concentrations. Using the PMF factor profiles and emission factors on the two freeways, we characterized the emission factors for light-duty and heavy-duty vehicles by each nontailpipe source. Our findings indicated higher nontailpipe emission factors of PM₁₀ and metal elements on the I-710 freeway compared to the I-110 freeway, due to the higher fraction of heavy-duty vehicles (HDVs) on that freeway. Furthermore, the generation of nontailpipe PM₁₀ from resuspension of road dust was twice of tire and brake wear. The results of this study provide significant insights into PM₁₀ freeway emissions and particularly the overall contribution of nontailpipe and tailpipe sources in Los Angeles, which can be helpful to modelers and air quality officials in assessing the importance of individual traffic-related emissions on the overall population exposure.

A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach

We investigate the impact of the COVID-19 outbreak on PM_2.5 levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM_2.5 levels over the contiguous U.S. in March-May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) μg/m³, and 2.04 (1.87) μg/m³, respectively. Results from Google Community Mobility Reports and estimated PM_2.5 concentrations show a greater reduction of PM_2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM_2.5 (i.e., the ratio of vehicular PM_2.5 to other sources of PM_2.5) emissions and PM_2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM_2.5 generally experience greater decreases in PM_2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM_2.5 levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March-May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM_2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM_2.5 emissions, highlighting the great impact of human activity on PM_2.5 changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO₂, SO₄, and especially NO₂, appear to have had a significantly greater impact on PM_2.5 changes during the study period.

Fine particulate matter pollution characteristics and source apportionment of Changchun atmosphere

In order to study the pollution characteristics and main sources of fine particulate matter in the atmosphere of the city of Changchun, PM_2.5 samples were collected during the four seasons in 2014, and representative months for each season are January, April, July, and October. Sample collection was carried out on 10 auto-monitoring stations in Changchun, and PM_2.5 mass concentration and its chemical components (including inorganic elements, organic carbon, elemental carbon, and water-soluble ions) were measured. The results show that the annual average mass concentration of PM_2.5 in Changchun in 2014 was about 66.77 μg/m³. Organic matter was the highest component in PM_2.5, followed by secondary inorganic ions (SNA), mineral dust (MIN), elemental carbon (EC), and trace elements (TE). Positive matrix factorization (PMF) results gave seven factors, namely, industrial, biomass and coal burning, industrial and soil dust, motor vehicle, soil and secondary ion, light industrial, and hybrid automotive and industrial sources in PM_2.5, with contributing values of 18.9%, 24.2%, 5.7%, 23.0%, 11.5%, 13.0%, and 3.6%, respectively.

Regulating light-duty vehicle emissions: an overview of US, EU, China and Brazil programs and its effect on air quality

This paper reviews the progress and effectiveness of Programs to Control Vehicle Emissions (PCVEs), comparing the experiences in the United States (US), European Union (EU), China, and Brazil. We present a timeline comparison of updates and differences in standards for light-duty vehicle (LDV) compliance. We then review the benefits of controlling LDV emissions on air quality, derived from previous relevant studies. Emission standards have been increasingly restricted in all evaluated PCVEs. However, some technical aspects such as dynamometer test cycles, re-testing structure of environmental protection agency, homogeneity of new and in-use vehicles inspection and maintenance, on-board diagnostics requirements are more consolidated in the US. Previous studies at different scales show the success of PCVEs in reducing vehicle emissions and air pollutant concentrations in the US, EU, China, and Brazil. Despite PCVEs has been achieving relative success, vehicular emissions are still a major threat to air quality around the world, especially in developing countries or ascending economies whose fleet grows dramatically. In places where the air quality standards recommended by the World Health Organization (WHO) are violated, it would be required the implementation of more stringent regulations with a well-designed, and homogeneous compliance policy over regional and national territories. This work contributes to clarifying the current challenges and successful experiences on regulating vehicular emissions worldwide.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10098-021-02238-1.

Air Pollution Particulate Matter Amplifies White Matter Vascular Pathology and Demyelination Caused by Hypoperfusion

Cerebrovascular pathologies are commonly associated with dementia. Because air pollution increases arterial disease in humans and rodent models, we hypothesized that air pollution would also contribute to brain vascular dysfunction. We examined the effects of exposing mice to nanoparticulate matter (nPM; aerodynamic diameter ≤200 nm) from urban traffic and interactions with cerebral hypoperfusion. C57BL/6 mice were exposed to filtered air or nPM with and without bilateral carotid artery stenosis (BCAS) and analyzed by multiparametric MRI and histochemistry. Exposure to nPM alone did not alter regional cerebral blood flow (CBF) or blood brain barrier (BBB) integrity. However, nPM worsened the white matter hypoperfusion (decreased CBF on DSC-MRI) and exacerbated the BBB permeability (extravascular IgG deposits) resulting from BCAS. White matter MRI diffusion metrics were abnormal in mice subjected to cerebral hypoperfusion and worsened by combined nPM+BCAS. Axonal density was reduced equally in the BCAS cohorts regardless of nPM status, whereas nPM exposure caused demyelination in the white matter with or without cerebral hypoperfusion. In summary, air pollution nPM exacerbates cerebrovascular pathology and demyelination in the setting of cerebral hypoperfusion, suggesting that air pollution exposure can augment underlying cerebrovascular contributions to cognitive loss and dementia in susceptible elderly populations.

Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.

Can new energy vehicles help to achieve carbon neutrality targets?

In the reforms pertaining to the energy structure in the automotive industry, new energy vehicles (NEVs) have long been the focus of government attention, as an effective means to reduce air pollution. Therefore, this paper employs the rolling-window Granger causality test, in order to discuss the environmental benefits of new energy vehicles, so as to explore the active role of the transportation sector in reducing air pollution. By studying the interactions between NEVs and particulate matter (PM_2.5) from the time period spanning from 2013:M1 to 2020:M9, we have found that the positive influences from NEVs to PM_2.5 ascertain that NEVs cannot be considered as an efficient measure to mitigate air pollution. Moreover, these results are not supported by the energy and environment interaction model, which essentially indicates that replacing traditional energy with renewable energy is an effective measure for controlling environmental pollution. In fact, PM_2.5 tends to have a negative impact on NEVs, which underlines that the air quality index is a leading indicator, particularly when it comes to analyzing the development of the NEVs market. This essentially highlights that in China, NEVs still do not account for a high proportion of car sales, and therefore, its environmental protection effect is not obvious. At the same time, the factor of public awareness regarding environmental protection will thus occupy a considerable proportion in the transmission of NEVs sales. These revelations will help the government to formulate environmental governance policies, and expand the new energy vehicle market to achieve carbon neutrality targets in China.

Beyond air pollution at home: Assessment of personal exposure to PM2.5 using activity-based travel demand model and low-cost air sensor network data

Assessing personal exposure to air pollution is challenging due to the limited availability of human movement data and the complexity of modeling air pollution at high spatiotemporal resolution. Most health studies rely on residential estimates of outdoor air pollution instead which introduces exposure measurement error. Personal exposure for 100,784 individuals in Los Angeles County was estimated by integrating human movement data simulated from the Southern California Association of Governments (SCAG) activity-based travel demand model with hourly PM_2.5 predictions from my 500 m gridded model incorporating low-cost sensor monitoring data. Individual exposures were assigned considering PM_2.5 levels at homes, workplaces, and other activity locations. These dynamic exposures were compared to the residence-based exposures, which do not consider human movement, to examine the degree of exposure estimation bias. The results suggest that exposures were underestimated by 13% (range 5-22%) on average when human movement was not considered, and much of the error was eliminated by accounting for work location. Exposure estimation bias increased for people who exhibited higher mobility levels, especially for workers with long commute distances. Overall, the personal exposures of workers were underestimated by 22% (5-61%) relative to their residence-based exposures. For workers who commute >20 miles, their exposure levels can be at most underestimated by 61%. Omitting mobility resulted in underestimating exposures for people who reside in areas with cleaner air but work in more polluted areas. Similarly, exposures were overestimated for people living in areas with poorer air quality and working in cleaner areas. These could lead to differential estimation biases across racial, ethnic and socioeconomic lines that typically correlate with where people live and work and lead to important exposure and health disparities. This study demonstrates that ignoring human movement and spatiotemporal variability of air pollution could lead to differential exposure misclassification potentially biasing health risk assessments. These improved dynamic approaches can help planners and policymakers identify disadvantaged populations for which exposures are typically misrepresented and might lead to targeted policy and planning implications.

Effects of Anthropogenic and Biogenic Volatile Organic Compounds on Los Angeles Air Quality

Assessing the role of volatile organic compounds (VOCs) in production of ozone and secondary organic aerosol (SOA) is especially important in light of ongoing policy goals. Here, we estimated the ozone formation potential (OFP) and SOA formation potential (SOAP) of anthropogenic and biogenic VOC emissions to evaluate (1) anthropogenic VOCs and associated sectors that dominate OFP and SOAP and (2) the potential impacts of enhanced biogenic VOCs from urban greening programs on air quality in Los Angeles county. In the present-day scenario, ethylene had the largest OFP followed by m & p-xylene, toluene, propylene, and formaldehyde. The top five contributors to SOAP were toluene, mineral spirits, benzene, heptadecane, and hexadecane. Mobile and solvent sources were the dominant VOC sources for both OFP and SOAP. The potential increases in biogenic VOC emissions due to future urban greening had significant effects on urban air quality that offset the benefits of reducing anthropogenic VOC emissions. This study demonstrates that urban greening programs in Los Angeles county, and likely other cities as well, need to account for both anthropogenic and biogenic VOC contributions to secondary pollution, and greening cities should consider using vegetation types with low VOC emissions to avoid further degradation to urban air quality.

Are standardized diesel exhaust particles (DEP) representative of ambient particles in air pollution toxicological studies?

In this study, we investigated the chemical characteristics of standardized diesel exhaust particles (DEP) and compared them to those of read-world particulate matter (PM) collected in different urban settings to evaluate the extent to which standardized DEPs can represent ambient particles for use in toxicological studies. Standard reference material SRM-2975 was obtained from the National Institute of Standards and Technology (NIST) and was chemically analyzed for the content of elemental carbon (EC), organic carbon (OC), polycyclic aromatic hydrocarbons (PAHs), inorganic ions, and several metals and trace elements. The analysis on the filter-collected DEP sample revealed very high levels of EC (i.e., ~397 ng/μg PM) which were comparable to the OC content (~405 ng/μg PM). This is in contrast with the carbonaceous content in the emitted particles from typical filter-equipped diesel-powered vehicles, in which low levels of EC emissions were observed. Furthermore, the EC mass fraction of the DEP sample did not match the observed levels in the ambient PM of multiple US urban areas, including Los Angeles (8%), Houston (~14%), Pittsburgh (~12%), and New York (~17%). Our results illustrated the lack of several high molecular weight carcinogenic PAHs in the DEP samples, unlike our measurements in major freeways of Los Angeles. Negligible levels of inorganic ions were observed in the sample and the DEP did not contain toxic secondary organic aerosols (SOAs) formed through synchronized reactions in the atmosphere. Lastly, the analysis of redox-active metals and trace elements demonstrated that the levels of many species including vehicle emission tracers (e.g., Ba, Ti, Mn, Fe) on Los Angeles roadways were almost 20 times greater than those in the DEP sample. Based on the abovementioned inconsistencies between the chemical composition of the DEP sample and those of real-world PM measured and recorded in different conditions, we conclude that the standardized DEPs are not suitable representatives of traffic emissions nor typical ambient PM to be used in toxicological studies.

Air Pollution Particulate Matter Exposure and Chronic Cerebral Hypoperfusion and Measures of White Matter Injury in a Murine Model

BACKGROUND

Exposure to ambient air pollution particulate matter (PM) is associated with increased risk of dementia and accelerated cognitive loss. Vascular contributions to cognitive impairment are well recognized. Chronic cerebral hypoperfusion (CCH) promotes neuroinflammation and blood-brain barrier weakening, which may augment neurotoxic effects of PM.

OBJECTIVES

This study examined interactions of nanoscale particulate matter (nPM; fine particulate matter with aerodynamic diameter ≤ 200  nm ) and CCH secondary to bilateral carotid artery stenosis (BCAS) in a murine model to produce white matter injury. Based on other air pollution interactions, we predicted synergies of nPM with BCAS.

METHODS

nPM was collected using a particle sampler near a Los Angeles, California, freeway. Mice were exposed to 10 wk of reaerosolized nPM or filtered air (FA) for 150 h. CCH was induced by BCAS surgery. Mice (C57BL/6J males) were randomized to four exposure paradigms: a) FA, b) nPM, c) FA   +   BCAS , and d) nPM   +   BCAS . Behavioral outcomes, white matter injury, glial cell activation, inflammation, and oxidative stress were assessed.

RESULTS

The joint nPM   +   BCAS group exhibited synergistic effects on white matter injury (2.3× the additive nPM and FA   +   BCAS scores) with greater loss of corpus callosum volume on T2 magnetic resonance imaging (MRI) (30% smaller than FA group). Histochemical analyses suggested potential microglial-specific inflammatory responses with synergistic effects on corpus callosum C5 immunofluorescent density and whole brain nitrate concentrations (2.1× and 3.9× the additive nPM and FA   +   BCAS effects, respectively) in the joint exposure group. Transcriptomic responses (RNA-Seq) showed greater impact of nPM   +   BCAS than individual additive effects, consistent with changes in proinflammatory pathways. Although nPM exposure alone did not alter working memory, the nPM   +   BCAS cohort demonstrated impaired working memory when compared to the FA   +   BCAS group.

DISCUSSION

Our data suggest that nPM and CCH contribute to white matter injury in a synergistic manner in a mouse model. Adverse neurological effects may be aggravated in a susceptible population exposed to air pollution. https://doi.org/10.1289/EHP8792.

Nanoparticulate matter exposure results in white matter damage and an inflammatory microglial response in an experimental murine model

Exposure to ambient air pollution has been associated with white matter damage and neurocognitive decline. However, the mechanisms of this injury are not well understood and remain largely uncharacterized in experimental models. Prior studies have shown that exposure to particulate matter (PM), a sub-fraction of air pollution, results in neuroinflammation, specifically the upregulation of inflammatory microglia. This study examines white matter and axonal injury, and characterizes microglial reactivity in the corpus callosum of mice exposed to 10 weeks (150 hours) of PM. Nanoscale particulate matter (nPM, aerodynamic diameter ≤200 nm) consisting primarily of traffic-related emissions was collected from an urban area in Los Angeles. Male C57BL/6J mice were exposed to either re-aerosolized nPM or filtered air for 5 hours/day, 3 days/week, for 10 weeks (150 hours; n = 18/group). Microglia were characterized by immunohistochemical double staining of ionized calcium-binding protein-1 (Iba-1) with inducible nitric oxide synthase (iNOS) to identify pro-inflammatory cells, and Iba-1 with arginase-1 (Arg) to identify anti-inflammatory/ homeostatic cells. Myelin injury was assessed by degraded myelin basic protein (dMBP). Oligodendrocyte cell counts were evaluated by oligodendrocyte transcription factor 2 (Olig2). Axonal injury was assessed by axonal neurofilament marker SMI-312. iNOS-expressing microglia were significantly increased in the corpus callosum of mice exposed to nPM when compared to those exposed to filtered air (2.2 fold increase; p<0.05). This was accompanied by an increase in dMBP (1.4 fold increase; p<0.05) immunofluorescent density, a decrease in oligodendrocyte cell counts (1.16 fold decrease; p<0.05), and a decrease in neurofilament SMI-312 (1.13 fold decrease; p<0.05) immunofluorescent density. Exposure to nPM results in increased inflammatory microglia, white matter injury, and axonal degradation in the corpus callosum of adult male mice. iNOS-expressing microglia release cytokines and reactive oxygen/ nitrogen species which may further contribute to the white matter damage observed in this model.

Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study

Predicting PM_2.5 concentrations at a fine spatial and temporal resolution (i.e., neighborhood, hourly) is challenging. Recent growth in low cost sensor networks is providing increased spatial coverage of air quality data that can be used to supplement data provided by monitors of regulatory agencies. We developed an hourly, 500 × 500 m gridded PM_2.5 model that integrates PurpleAir low-cost air sensor network data for Los Angeles County. We developed a quality control scheme for PurpleAir data. We included spatially and temporally varying predictors in a random forest model with random oversampling of high concentrations to predict PM_2.5. The model achieved high prediction accuracy (10-fold cross-validation (CV) R² = 0.93, root mean squared error (RMSE) = 3.23 μg/m³; spatial CV R² = 0.88, spatial RMSE = 4.33 μg/m³; temporal CV R² = 0.90, temporal RMSE = 3.85 μg/m³). Our model was able to predict spatial and diurnal patterns in PM_2.5 on typical weekdays and weekends, as well as non-typical days, such as holidays and wildfire days. The model allows for far more precise estimates of PM_2.5 than existing methods based on few sensors. Taking advantage of low-cost PM_2.5 sensors, our hourly random forest model predictions can be combined with time-activity diaries in future studies, enabling geographically and temporally fine exposure estimation for specific population groups in studies of acute air pollution health effects and studies of environmental justice issues.

Association of systemic inflammation and coagulation biomarkers with source-specific PM2.5 mass concentrations among young and elderly subjects in central Tehran

In this study, we investigated the association between short-term exposure to different sources of fine particulate matter (PM_2.5) and biomarkers of coagulation and inflammation in two different panels of elderly and healthy young individuals in central Tehran. Five biomarkers, including white blood cells (WBC), high sensitive C-reactive protein (hsCRP), tumor necrosis factor-soluble receptor-II (sTNF-RII), interleukin-6 (IL-6), and von Willebrand factor (vWF) were analyzed in the blood samples drawn every 8 weeks from the subjects between May 2012 and May 2013. The studied populations consisted of 44 elderly individuals at a retirement home as well as 40 young adults residing at a school dormitory. Positive Matrix Factorization (PMF)-resolved source-specific PM_2.5 mass concentrations and biomarker levels were used as the input to the linear mixed-effects regression model to evaluate the impact of exposure to previously identified PM sources at retirement home and school dormitory in two time lag configurations: lag 1-3 (1-3 days before the blood sampling), and lag 4-6 (4-6 days before the blood sampling). Our analysis of the elderly revealed positive associations of all biomarkers (except hsCRP) with particles of secondary origin in both time lags, further corroborating the toxicity of secondary aerosols formed by photochemical processing in central Tehran. Moreover, industrial emissions, and road dust particles were positively associated with WBC, sTNF-RII, and IL-6 among seniors, while vehicular emissions exhibited positive associations with all biomarkers in either first- or second-time lag. In contrast, most of the PM_2.5 sources showed insignificant associations with biomarkers of inflammation in the panel of healthy young subjects. Therefore, findings from this study indicated that various PM_2.5 sources increase the levels of inflammation and coagulation biomarkers, although the strength and significance of these associations vary depending on the type of PM sources, demographic characteristics, and differ across the different time lags. Implications: Tehran, the capital of Iran with a population of more than 9 million people, has been facing serious air pollution challenges as a result of extensive vehicular, and industrial activities in the previous years. Among various air pollutants in Tehran, fine particulate matters (PM_2.5, particles with aerodynamic diameters < 2.5 µm) are known as one of the most important critical pollutants, causing several adverse health impacts including lung cancer, respiratory, cardiovascular, and cardiopulmonary diseases. Therefore, a number of studies in the area have tried to investigate the adverse health impacts of exposure to PM_2.5. However, no studies have ever been conducted in Tehran to examine the association between specific PM_2.5 sources and biomarkers of coagulation and systemic inflammation as indicators of cardiovascular disorders. Indeed, this is the first study in the area investigating the association of source-specific PM_2.5 with biomarkers of inflammation including white blood cells (WBC), high sensitive C-reactive protein (hsCRP), tumor necrosis factor-soluble receptor-II (sTNF-RII), interleukin-6 (IL-6), and von Willebrand factor (vWF). Our results have important implications for policy makers in identifying the most toxic sources of PM_2.5, and in turn designing schemes for mitigating adverse health impacts of air pollution in Tehran.

Contribution of tailpipe and non-tailpipe traffic sources to quasi-ultrafine, fine and coarse particulate matter in southern California

Exposure to traffic-related air pollution (TRAP) in the near-roadway environment is associated with multiple adverse health effects. To characterize the relative contribution of tailpipe and non-tailpipe TRAP sources to particulate matter (PM) in the quasi-ultrafine (PM_0.2), fine (PM_2.5) and coarse (PM_2.5-10) size fractions and identify their spatial determinants in southern California (CA). Month-long integrated PM_0.2, PM_2.5 and PM_2.5-10 samples (n = 461, 265 and 298, respectively) were collected across cool and warm seasons in 8 southern CA communities (2008-9). Concentrations of PM mass, elements, carbons and major ions were obtained. Enrichment ratios (ER) in PM_0.2 and PM₁₀ relative to PM_2.5 were calculated for each element. The Positive Matrix Factorization model was used to resolve and estimate the relative contribution of TRAP sources to PM in three size fractions. Generalized additive models (GAMs) with bivariate loess smooths were used to understand the geographic variation of TRAP sources and identify their spatial determinants. EC, OC, and B had the highest median ER in PM_0.2 relative to PM_2.5. Six, seven and five sources (with characteristic species) were resolved in PM_0.2, PM_2.5 and PM_2.5-10, respectively. Combined tailpipe and non-tailpipe traffic sources contributed 66%, 32% and 18% of PM_0.2, PM_2.5 and PM_2.5-10 mass, respectively. Tailpipe traffic emissions (EC, OC, B) were the largest contributor to PM_0.2 mass (58%). Distinct gasoline and diesel tailpipe traffic sources were resolved in PM_2.5. Others included fuel oil, biomass burning, secondary inorganic aerosol, sea salt, and crustal/soil. CALINE4 dispersion model nitrogen oxides, trucks and intersections were most correlated with TRAP sources. The influence of smaller roadways and intersections became more apparent once Long Beach was excluded. Non-tailpipe emissions constituted ~8%, 11% and 18% of PM_0.2, PM_2.5 and PM_2.5-10, respectively, with important exposure and health implications. Future efforts should consider non-linear relationships amongst predictors when modeling exposures. Implications: Vehicle emissions result in a complex mix of air pollutants with both tailpipe and non-tailpipe components. As mobile source regulations lead to decreased tailpipe emissions, the relative contribution of non-tailpipe traffic emissions to near-roadway exposures is increasing. This study documents the presence of non-tailpipe abrasive vehicular emissions (AVE) from brake and tire wear, catalyst degradation and resuspended road dust in the quasi-ultrafine (PM_0.2), fine and coarse particulate matter size fractions, with contributions reaching up to 30% in PM_0.2 in some southern California communities. These findings have important exposure and policy implications given the high metal content of AVE and the efficiency of PM_0.2 at reaching the alveolar region of the lungs and other organ systems once inhaled. This work also highlights important considerations for building models that can accurately predict tailpipe and non-tailpipe exposures for population health studies.

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.

Long-term trends in the contribution of PM2.5 sources to organic carbon (OC) in the Los Angeles basin and the effect of PM emission regulations

This study aimed to investigate the long-term variations in the contributions of emission sources to ambient PM_2.5 organic carbon (OC) in central Los Angeles (CELA) and Riverside using the Chemical Speciation Network (CSN) database in the 2005-2015 period, during which several federal and state PM-based regulations were implemented to reduce tailpipe emissions in the region. The measured concentrations of OC, OC volatility fractions (i.e., OC₁, OC₂, and OC₃), elemental carbon (EC), ozone (O₃), sulfate, the ratio of potassium ion to potassium (K⁺/K), and selected metal elements were used as the input to the positive matrix factorization (PMF) model. PMF resolved tailpipe emissions, non-tailpipe emissions, secondary organic aerosols (SOA), biomass burning, and local industrial activities as the main sources contributing to ambient OC at both sampling sites. Vehicular exhaust emissions, non-tailpipe emissions, and SOA were dominant sources of OC across our sampling sites, accounting cumulatively for more than 80% of total OC mass throughout the study period. Our findings showed a significant reduction in the absolute and relative contributions of tailpipe emissions to the ambient OC levels in CELA and Riverside over the time period of 2005-2015. The contribution of exhaust emissions to total OC in CELA decreased from 3.5 µg m^-3 (49%) in 2005 to 1.5 µg m^-3 (34%) in 2015, while similar trends were observed at Riverside during this period. These reductions are mainly attributed to the implementation of several federal, state, and local air quality regulations targeting tailpipe emissions in the area. The implementation of these regulations furthermore reduced the emissions of primary organic precursors of secondary aerosols, resulting in an overall decrease (although not statistically significant, P values ranging from 0.4 to 0.6) in SOA mass concentration in both locations over the study period. In contrast to the tailpipe emissions, we observed an increasing trend (by ∼4 to 14%) in the relative contribution of non-tailpipe emissions to OC over this time period at both sites. Our results demonstrated the effectiveness of air quality regulations in reducing direct tailpipe emissions in the area, but also underpinned the need to develop equally effective mitigation policies targeting non-tailpipe PM emissions.

Positive matrix factorization of ultrafine particle mass (PM0.1) at three sites in California

Ultrafine particles (UFPs) are an emerging air quality concern because of their enhanced toxicity compared to larger airborne particles. This study aims to better understand source contributions to UFP mass (PM_0.1) at multiples sites across California. Three-day average samples of PM_0.1 collected over a full year at San Pablo, East Oakland, and Los Angeles were analyzed using Positive Matrix Factorization (PMF). Seven PM_0.1 source-factors were identified at all locations: Factor1- Gasoline+Motor Oil+Meat Cooking+Natural Gas+SOA (31-53% PM_0.1 mass), Factor 2- Diesel+Motor Oil (25-45% PM_0.1 mass), Factor 3-Wood Burning (6-12% PM_0.1 mass), Factor 4-Shipping and other heavy fuel oil combustion (2-3% PM_0.1 mass), Factor 5-Sea Spray (4-8% PM_0.1 mass), Factor 6-Sb Brake Wear (1-3% PM_0.1 mass) and Factor 7-Sn - Unknown (1-7% PM_0.1 mass). PM_0.1 wood burning contributions were highest in the winter season when residential wood combustion was active. The monthly-averaged PM_0.1 source apportionment results calculated by PMF are consistent with the PM_0.1 source apportionment results calculated using Chemical Mass Balance (CMB) from the same sampling campaign. PMF distinguished Diesel+Motor Oil from Gasoline+Motor Oil+Meat Cooking+Natural Gas+SOA based on the species EC3 (a sub-fraction of elemental carbon that is volatilized and oxidized at temperatures between 700 and 775 °C), but PMF failed to further resolve the major sources of PM_0.1 OC because unique tracers were not measured. PMF resolved "Shipping and other heavy fuel oil combustion" and Sea Spray sources based on inorganic tracers V and Br. The PMF factor rich in Sb very likely comes from brake wear associated with on-road vehicles and railway operations. The undefined Sn factor may be indicative of local industrial sources and traffic emission, but further research will be required to confirm this hypothesis. The PM_0.1 source apportionment results contained in the current study further characterize the seasonal and spatial patterns of UFP concentrations in California.

Ambient particulate matter in Santiago, Chile: 1989-2018: A tale of two size fractions

We have analyzed trends in ambient fine (PM_2.5) and coarse (PM_2.5-10) particulate matter in Santiago, Chile, for the last 30 years. PM_2.5 has monotonously decreased between 67% and 72% at those sites. Trends varied between -2.0 and -2.7 (μg/m³/year) between 1989 and late 90's, and between -0.7 and -1.1 (μg/m³/year) afterwards. This slowing down is likely a consequence of fast increase of motor vehicles in the city, which have become a dominant source of ambient PM_2.5. Annual ambient PM_2.5 concentrations are still above 20 (μg/m³), so more regulation is needed to bring them down. Coarse particles have changed little in 30 years, decreasing between 0% and 12%; particle concentrations have evolved in a non-linear way: first increasing in 1989-1995, then decreasing until 2003, and with a flat trend afterwards. We ascribe these trends to a combination of a) public works implemented throughout the city, b) fugitive dust controls like street sweeping programs and emission offsets for PM₁₀ and c) increasing numbers of motor vehicles in the city. Further initiatives are needed to curb down coarse particles as well. By considering interaction between trend and seasonality, we have found that ambient PM_2.5 has monotonously decreased all year long at all monitoring sites with similar patterns; this is characteristic of a regional-scale pollution. For ambient PM_2.5-10 trend and season have a more complex, site-specific interaction, suggesting local sources and site location in the basin are relevant in determining ambient concentrations of coarse particles. A limitation of this study is that no quantitative link between ambient concentrations trends and atmospheric emissions could be established with the analyses carried out. A strength of the study is the long period analyzed with measurements conducted with the same gravimetric methodology.

Impact of secondary and primary particulate matter (PM) sources on the enhanced light absorption by brown carbon (BrC) particles in central Los Angeles

In this study, we investigated aerosol chemical composition, spectral properties of aerosol extracts, and source contributions to the aerosol light-absorbing brown carbon (BrC) in central Los Angeles from July 2018 to March 2019, during warm and cold seasons. Spectrophotometric measurements (water and methanol extracts; 200 < λ < 1100) and chemical analyses were performed on collected particulate matter (PM), and relationships of BrC light absorption (Abs₃₆₅) to source tracer chemical species were evaluated. Mass absorption efficiency (MAE) of both water and methanol extracted solutions exhibited an increasing trend from warm period to cold season, with an annual average value of 0.61 ± 0.22 m².g^-1 and 1.38 ± 0.89 m².g^-1, respectively. Principal component analysis (PCA) were coupled with multiple linear regression (MLR) to identify and quantify sources of BrC light absorption in each of the seasons. Our finding documented fossil fuel combustion as the dominant source of BrC light absorption during warm season, with relative contribution of 38% to total BrC light absorption, followed by (secondary organic aerosol) SOA (30%) and biomass burning (12%). In contrast, biomass burning was the major source of BrC during the cold season (53%), while fossil fuel combustion and SOA contributed to 18% and 12% of BrC, respectively. Significantly higher contribution of biomass burning to BrC during the cold season suggested that residential heating activities (wood burning) play a major role in increased BrC concentrations. Previously collected Aethalometer model data documented fossil fuel combustion as the dominant contributing source to >90% of BC throughout the year. Finally, the solar radiation absorption ratio of BrC to elemental carbon (EC) in the ultraviolet range (300-400 nm) was maximum during the cold season with the annual corresponding values of 13-25% and 17-29% for water- and methanol-soluble BrC, respectively; which provides further evidence of the important effect of BrC light absorption on atmospheric radiative balance.

Semi-volatile components of PM2.5 in an urban environment: volatility profiles and associated oxidative potential

The volatility profiles of PM2.5 semi-volatile compounds and relationships to the oxidative potential of urban airborne particles were investigated in central Los Angeles, CA. Ambient and thermodenuded fine (PM2.5) particles were collected during both warm and cold seasons by employing the Versatile Aerosol Concentration Enrichment System (VACES) combined with a thermodenuder. When operated at 50 °C and 100 °C, the VACES/thermodenuder system removed about 50% and 75% of the PM2.5 volume concentration, respectively. Most of the quantified PM2.5 semi-volatile species including organic carbon (OC), water soluble organic carbon (WSOC), polycyclic aromatic hydrocarbons (PAHs), organic acids, n-alkanes, and levoglucosan, as well as inorganic ions (i.e., nitrate, sulfate, and ammonium) exhibited concentration losses in the ranges of 40-66% and 67-92%, respectively, as the thermodenuder temperature increased to 50 °C and 100 °C. Species in the PM2.5 such as elemental carbon (EC) and inorganic elements (including trace metals) were minimally impacted by the heating process - thus can be considered refractory. On average, nearly half of the PM2.5 oxidative potential (as measured by the dichlorodihydrofluorescein (DCFH) alveolar macrophage in vitro assay) was associated with the semi-volatile species removed by heating the aerosols to only 50 °C, highlighting the importance of this quite volatile compartment to the ambient PM2.5 toxicity. The fraction of PM2.5 oxidative potential lost upon heating the aerosols to 100 °C further increased to around 75-85%. Furthermore, we document statistically significant correlations between the PM2.5 oxidative potential and different semi-volatile organic compounds originating from primary and secondary sources, including OC (Rwarm, and Rcold) (0.86, and 0.74), WSOC (0.60, and 0.98), PAHs (0.88, and 0.76), organic acids (0.76, and 0.88), and n-alkanes (0.67, and 0.83) in warm and cold seasons, respectively, while a strong correlation between oxidative potential and levoglucosan, a tracer of biomass burning, was observed only during the cold season (Rcold=0.81).

Land use regression models for ultrafine particles, fine particles, and black carbon in Southern California

Exposure models are needed to evaluate health effects of long-term exposure to ambient ultrafine particles (UFP; <0.1 μm) and to disentangle their association from other pollutants, particularly PM_2.5 (<2.5 μm). We developed land use regression (LUR) models to support UFP exposure assessment in the Los Angeles Ultrafines Study, a cohort in Southern California. We conducted a short-term measurement campaign in Los Angeles and parts of Riverside and Orange counties to measure UFP, PM_2.5, and black carbon (BC), collecting three 30-minute average measurements at 215 sites across three seasons. We averaged concentrations for each site and evaluated geographic predictors including traffic intensity, distance to airports, land use, and population and building density by supervised stepwise selection to develop models. UFP and PM_2.5 measurements (r = 0.001) and predictions (r = 0.05) were uncorrelated at the sites. UFP model explained variance was robust (R² = 0.66) and 10-fold cross-validation indicated good performance (R² = 0.59). Explained variation was moderate for PM_2.5 (R² = 0.47) and BC (R² = 0.38). In the cohort, we predicted a 2.3-fold exposure contrast from the 5^th to 95^th percentiles for all three pollutants. The correlation between modeled UFP and PM_2.5 at cohort residences was weak (r = 0.28), although higher than between measured levels. LUR models, particularly for UFP, were successfully developed and predicted reasonable exposure contrasts.

Characterization of laboratory and real driving emissions of individual Euro 6 light-duty vehicles - Fresh particles and secondary aerosol formation

Emissions from passenger cars are one of major sources that deteriorate urban air quality. This study presents characterization of real-drive emissions from three Euro 6 emission level passenger cars (two gasoline and one diesel) in terms of fresh particles and secondary aerosol formation. The gasoline vehicles were also characterized by chassis dynamometer studies. In the real-drive study, the particle number emissions during regular driving were 1.1-12.7 times greater than observed in the laboratory tests (4.8 times greater on average), which may be caused by more effective nucleation process when diluted by real polluted and humid ambient air. However, the emission factors measured in laboratory were still much higher than the regulatory value of 6 × 10¹¹ particles km^-1. The higher emission factors measured here result probably from the fact that the regulatory limit considers only non-volatile particles larger than 23 nm, whereas here, all particles (also volatile) larger than 3 nm were measured. Secondary aerosol formation potential was the highest after a vehicle cold start when most of the secondary mass was organics. After the cold start, the relative contributions of ammonium, sulfate and nitrate increased. Using a novel approach to study secondary aerosol formation under real-drive conditions with the chase method resulted mostly in emission factors below detection limit, which was not in disagreement with the laboratory findings.

Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors

A chance constrained stochastic Data Envelopment Analysis (DEA) was developed for investigating the relations between PM_2.5 pollution days and meteorological factors and human activities, incorporating with an empirical study for 13 cities in Jiangsu Province (China) to illustrate the model. This approach not only admits random input and output environment, but also allows the evaluation unit to exceed the front edge under the given probability constraint. Moreover, observing the change in outcome variables when a group of explanatory variables are deleted provides an additional strategic technique to measure the effect of the remaining explanatory variables. It is found that: (1) For 2013–2016, the influencing factors of PM_2.5 pollution days included wind speed, no precipitation day, relative humidity, population density, construction area, transportation, coal consumption and green coverage rate. In 2016, the number of cities whose PM_2.5 pollution days was affected by construction was decreased by three from 2015 but increased according to transportation and energy utilization. (2) The PM_2.5 pollution days in southern and central Jiangsu Province were primarily affected by the combined effect of the meteorological factors and social progress, while the northern Jiangsu Province was largely impacted by the social progress. In 2013–2016, at different risk levels, 60% inland cities were of valid stochastic efficiency, while 33% coastal cities were of valid stochastic efficiency. (3) The chance constrained stochastic DEA, which incorporates the data distribution characteristics of meteorological factors and human activities, is valuable for exploring the essential features of data in investigating the influencing factors of PM_2.5.

Ultrafine particles and PM2.5 in the air of cities around the world: Are they representative of each other?

Can mitigating only particle mass, as the existing air quality measures do, ultimately lead to reduction in ultrafine particles (UFP)? The aim of this study was to provide a broader urban perspective on the relationship between UFP, measured in terms of particle number concentration (PNC) and PM_2.5 (mass concentration of particles with aerodynamic diameter < 2.5 μm) and factors that influence their concentrations. Hourly average PNC and PM_2.5 were acquired from 10 cities located in North America, Europe, Asia, and Australia over a 12-month period. A pairwise comparison of the mean difference and the Kolmogorov-Smirnov test with the application of bootstrapping were performed for each city. Diurnal and seasonal trends were obtained using a generalized additive model (GAM). The particle number to mass concentration ratios and the Pearson's correlation coefficient were calculated to elucidate the nature of the relationship between these two metrics. Results show that the annual mean concentrations ranged from 8.0 × 10³ to 19.5 × 10³ particles·cm^-3 and from 7.0 to 65.8 μg·m^-3 for PNC and PM_2.5, respectively, with the data distributions generally skewed to the right, and with a wider spread for PNC. PNC showed a more distinct diurnal trend compared with PM_2.5, attributed to the high contributions of UFP from vehicular emissions to PNC. The variation in both PNC and PM_2.5 due to seasonality is linked to the cities' geographical location and features. Clustering the cities based on annual median concentrations of both PNC and PM_2.5 demonstrated that a high PNC level does not lead to a high PM_2.5, and vice versa. The particle number-to-mass ratio (in units of 10⁹ particles·μg^-1) ranged from 0.14 to 2.2, >1 for roadside sites and <1 for urban background sites with lower values for more polluted cities. The Pearson's r ranged from 0.09 to 0.64 for the log-transformed data, indicating generally poor linear correlation between PNC and PM_2.5. Therefore, PNC and PM_2.5 measurements are not representative of each other; and regulating PM_2.5 does little to reduce PNC. This highlights the need to establish regulatory approaches and control measures to address the impacts of elevated UFP concentrations, especially in urban areas, considering their potential health risks.

Associations of Source-apportioned Fine Particles with Cause-specific Mortality in California

BACKGROUND

Exposure to ambient fine particulate matter (PM2.5) has been linked with premature mortality, but sources of PM2.5 have been less studied.

METHODS

We evaluated associations between source-specific PM2.5 exposures and cause-specific short-term mortality in eight California locations from 2002 to 2011. Speciated PM2.5 measurements were source-apportioned using Positive Matrix Factorization into eight sources and combined with death certificate data. We used time-stratified case-crossover analysis with conditional logistic regression by location and meta-analysis to calculate pooled estimates.

RESULTS

Biomass burning was associated with all-cause mortality lagged 2 days after exposure (lag2) (% changelag2 in odds per interquartile range width increase in biomass burning PM2.5 = 0.8, 95% confidence interval [CI] = 0.2, 1.4), cardiovascular (% changelag2 = 1.3, 95% CI = 0.3, 2.4), and ischemic heart disease (% changelag2 = 2.0, 95% CI = 0.6, 3.5). Vehicular emissions were associated with increases in cardiovascular mortality (% changelag0 = 1.4, 95% CI = 0.0, 2.9). Several other sources exhibited positive associations as well. Many findings persisted during the cool season. Warm season biomass burning was associated with respiratory/thoracic cancer mortality (% changelag1 = 5.9, 95% CI = 0.7, 11.3), and warm season traffic was associated with all-cause (% changelag0 = 1.9, 95% CI = 0.1, 3.6) and cardiovascular (% changelag0 = 2.9, 95% CI = 0.1, 5.7) mortality.

CONCLUSIONS

Our results suggest that acute exposures to biomass burning and vehicular emissions are linked with cardiovascular mortality, with additional sources (i.e., soil, secondary nitrate, secondary sulfate, aged sea salt, and chlorine sources) showing associations with other specific mortality types.

Spatio-temporal trends and source apportionment of fossil fuel and biomass burning black carbon (BC) in the Los Angeles Basin

In this study, we evaluated the spatial and temporal trends of black carbon (BC) in the Los Angeles Basin between 2012-2013 and 2016-2017. BC concentrations were measured in seven wavelengths using Aethalometers (AE33) at four sites, including central Los Angeles (CELA), Anaheim, Fontana, and Riverside. Sources of BC were quantified using the equivalent black carbon (EBC) model. Results indicate that total BC concentrations nearly doubled in colder period compared to the warm period. Source apportionment results revealed that fossil fuel combustion has higher annual contributions (ranging from 82% in Riverside to 91% in CELA) than biomass burning (ranging from 9.3% in CELA to 18.7% in Riverside) to the total BC concentrations at all sites. This trend was more clearly observed at the sites closer to major freeways, such as CELA and Anaheim. The relative contribution of fossil fuel to total BC concentrations was higher in the warm period, whereas biomass burning had higher contributions in the colder period. The diurnal variation of fossil-fuel-originated BC (BC_ff) to the total BC concentrations revealed major rises during the traffic rush hours, especially in the warm period. In contrast, the fraction of BC originating from biomass burning (BC_bb) peaked at nighttime, particularly in the cold period, reaching values as high as 25-30% of total BC concentration. Moreover, we observed a clear decrease in both absolute BC concentrations as well as relative contributions of BC_ff to total BC concentrations from 2012-2013 to 2016-2017, which can be attributed to the implementation of strict regulations in California to reduce transportation-related PM emissions. Results from the present study suggest that as these regulations become increasingly stricter, the relative contributions of traffic sources to BC also decrease, thereby making the impact of non-fossil fuel combustion sources, such as biomass burning, to the overall BC levels more significant.

Source apportionment of ambient PM2.5 in two locations in central Tehran using the Positive Matrix Factorization (PMF) model

In this study, the positive matrix factorization (PMF) model was used for source apportionment of ambient PM_2.5 in two locations in the central Tehran from May 2012 through June 2013. The average PM_2.5 mass concentrations were 30.9 and 33.2μg/m³ in Tohid retirement home and the school dormitory, respectively. Metals and trace elements, water-soluble ions, and PM_2.5 mass concentrations were used as inputs to the model. Concentrations of elemental and organic carbon (EC and OC), and meteorological data were also used as auxiliary variables to help with the factor identification and interpretation. A 7-factor solution was identified as the best solution for both sites. The identified source factors included vehicular emissions, secondary aerosol, industrial emissions, biomass burning, soil, and road dust (including tire and brake wear particles) in both sampling sites. Results indicated that almost half of PM_2.5 mass can be attributed to vehicular emissions at both sites. Secondary aerosol was the second major contributor to PM_2.5 mass concentrations at both sites, with contributions of around 25% on average for both sites. In addition, while two industrial factors were identified in Tohid retirement home (with an overall contribution of 17%), only one industrial factor (with a minimal contribution of <2%) was identified at Tohid retirement home, probably due to the fact that the retirement home is impacted to a higher degree by industry-related activities. The other factors included biomass burning, road dust, and soil, with overall contributions of around 20% in both sites. Results of this study clearly indicate the major role of traffic-related emissions (both tailpipe and non-tailpipe) on ambient PM_2.5 concentrations, and can be used as a beneficial tool for air quality policy makers to mitigate adverse health effects of exposure to PM_2.5.

Long-term trends and health impact of PM2.5 and O3 in Tehran, Iran, 2006-2015

The main objectives of this study were (1) investigation of the temporal variations of ambient fine particulate matter (PM_2.5) and ground level ozone (O₃) concentrations in Tehran megacity, the capital and most populous city in Iran, over a 10-year period from 2006 to 2015, and (2) estimation of their long-term health effects including all-cause and cause-specific mortality. For the first goal, the data of PM_2.5 and O₃ concentrations, measured at 21 regulatory monitoring network stations in Tehran, were obtained and the temporal trends were investigated. The health impact assessment of PM_2.5 and O₃ was performed using the World Health Organization (WHO) AirQ+ software updated in 2016 by WHO European Centre for Environment and Health. Local baseline incidences in Tehran level were used to better reveal the health effects associated with PM_2.5 and O₃. Our study showed that over 2006-2015, annual mean concentrations of PM_2.5 and O₃ varied from 24.7 to 38.8 μg m^-3 and 35.4 to 76.0 μg m^-3, respectively, and were significantly declining in the recent 6 years (2010-2015) for PM_2.5 and 8 years (2008-2015) for O₃. However, Tehran citizens were exposed to concentrations of annual PM_2.5 exceeding the WHO air quality guideline (WHO AQG) (10 μg m^-3), U.S. EPA and Iranian standard levels (12 μg m^-3) during entire study period. We estimated that long-term exposure to ambient PM_2.5 contributed to between 24.5% and 36.2% of mortality from cerebrovascular disease (stroke), 19.8% and 24.1% from ischemic heart disease (IHD), 13.6% and 19.2% from lung cancer (LC), 10.7% and 15.3% from chronic obstructive pulmonary disease (COPD), 15.0% and 25.2% from acute lower respiratory infection (ALRI), and 7.6% and 11.3% from all-cause annual mortality in the time period. We further estimated that deaths from IHD accounted for most of mortality attributable to long-term exposure to PM_2.5. The years of life lost (YLL) attributable to PM_2.5 was estimated to vary from 67,970 to 106,706 during the study period. In addition, long-term exposure to O₃ was estimated to be responsible for 0.9% to 2.3% of mortality from respiratory diseases. Overall, long-term exposure to ambient PM_2.5 and O₃ contributed substantially to mortality in Tehran megacity. Air pollution is a modifiable risk factor. Appropriate sustainable control policies are recommended to protect public health.

Urban local air quality management framework for non-attainment areas in Indian cities

Increasing urban air pollution level in Indian cities is one of the major concerns for policy makers due to its impact on public health. The growth in population and increase in associated motorised road transport demand is one of the major causes of increasing air pollution in most urban areas along with other sources e.g., road dust, construction dust, biomass burning etc. The present study documents the development of an urban local air quality management (ULAQM) framework at urban hotspots (non-attainment area) and a pathway for the flow of information from goal setting to policy making. The ULAQM also includes assessment and management of air pollution episodic conditions at these hotspots, which currently available city/regional-scale air quality management plans do not address. The prediction of extreme pollutant concentrations using a hybrid model differentiates the ULAQM from other existing air quality management plans. The developed ULAQM framework has been applied and validated at one of the busiest traffic intersections in Delhi and Chennai cities. Various scenarios have been tested targeting the effective reductions in elevated levels of NO_x and PM_2.5 concentrations. The results indicate that a developed ULAQM framework is capable of providing an evidence-based graded action to reduce ambient pollution levels within the specified standard level at pre-identified locations. The ULAQM framework methodology is generalised and therefore can be applied to other non-attainment areas of the country.

Cause-specific stillbirth and exposure to chemical constituents and sources of fine particulate matter

The stillbirth rate in the United States is relatively high, but limited evidence is available linking stillbirth with fine particulate matter (PM_2.5), its chemical constituents and sources. In this study, we explored associations between cause-specific stillbirth and prenatal exposures to those pollutants with using live birth and stillbirth records from eight California locations during 2002-2009. ICD-10 codes were used to identify cause of stillbirth from stillbirth records. PM_2.5 total mass and chemical constituents were collected from ambient monitors and PM_2.5 sources were quantified using Positive Matrix Factorization. Conditional logistic regression was applied using a nested case-control study design (N = 32,262). We found that different causes of stillbirth were associated with different PM_2.5 sources and/or chemical constituents. For stillbirths due to fetal growth, the odds ratio (OR) per interquartile range increase in gestational age-adjusted exposure to PM_2.5 total mass was 1.23 (95% confidence interval (CI): 1.06, 1.44). Similar associations were found with resuspended soil (OR=1.25, 95% CI: 1.10, 1.42), and secondary ammonium sulfate (OR=1.45, 95% CI: 1.18, 1.78). No associations were found between any pollutants and stillbirths caused by maternal complications. This study highlighted the importance of investigating cause-specific stillbirth and the differential toxicity levels of specific PM_2.5 sources and chemical constituents.

Source apportionment of fine particulate matter and risk of term low birth weight in California: Exploring modification by region and maternal characteristics

Previous studies have demonstrated associations between fine particulate matter (PM_2.5) and risk of term low birth weight (TLBW; birth weight<2500g and gestational weeks≥37weeks). However, it remains unclear which PM_2.5 sources mainly contribute to these associations, and which subgroups (e.g. by residential region and maternal characteristics) may be more susceptible to these exposures. Using California birth records and PM_2.5 data from eight monitoring sites from 2002 to 2009, we examined the relationship between exposures to total PM_2.5 and PM_2.5 sources and risk of TLBW. Source apportionment was performed for each site using Positive Matrix Factorization, and five PM_2.5 sources (i.e., secondary ammonium sulfate, secondary ammonium nitrate, vehicular emissions, biomass burning, and resuspended soil) were included in our analysis. Mean gestational and trimester exposures were calculated for mothers with ZIP codes located within a 20km radius of monitors (N=1,050,330). Logistic regression was conducted and adjusted for maternal age, race/ethnicity, and education, as well as gestational age, year of birth, apparent temperature exposure during gestation, and neighborhood level percentage of households below poverty level. Increased risks of TLBW associated with each interquartile range increase in exposure were 4.9% (95% confidence interval: 2.6, 7.3) for total PM_2.5, 7.7% (4.7, 10.7) for secondary ammonium sulfate, 5.6% (3.5, 7.7) for resuspended soil, and 3.1% (1.3, 4.9) for secondary ammonium nitrate. Differences in associations were found between inland and coastal regions, and between northern and southern regions for several sources. Results also showed effect measure modification by maternal race/ethnicity and education, with the lowest risk of TLBW associated with PM_2.5 exposures found in mothers with at least a college education and Asian mothers. Some PM_2.5 sources may be more harmful than others, and a better understanding of the relative toxicity of PM_2.5 from each source could lead to more targeted and cost-effective regulations to protect public health.

Source apportionment and health risk assessment among specific age groups during haze and non-haze episodes in Kuala Lumpur, Malaysia

This study aims to determine PM_2.5 concentrations and their composition during haze and non-haze episodes in Kuala Lumpur. In order to investigate the origin of the measured air masses, the Numerical Atmospheric-dispersion Modelling Environment (NAME) and Global Fire Assimilation System (GFAS) were applied. Source apportionment of PM_2.5 was determined using Positive Matrix Factorization (PMF). The carcinogenic and non-carcinogenic health risks were estimated using the United State Environmental Protection Agency (USEPA) method. PM_2.5 samples were collected from the centre of the city using a high-volume air sampler (HVS). The results showed that the mean PM_2.5 concentrations collected during pre-haze, haze and post-haze periods were 24.5±12.0μgm^-3, 72.3±38.0μgm^-3 and 14.3±3.58μgm^-3, respectively. The highest concentration of PM_2.5 during haze episode was five times higher than World Health Organisation (WHO) guidelines. Inorganic compositions of PM_2.5, including trace elements and water soluble ions were determined using inductively coupled plasma-mass spectrometry (ICP-MS) and ion chromatography (IC), respectively. The major trace elements identified were K, Al, Ca, Mg and Fe which accounted for approximately 93%, 91% and 92% of the overall metals' portions recorded during pre-haze, haze and post-haze periods, respectively. For water-soluble ions, secondary inorganic aerosols (SO₄^2-, NO₃^- and NH₄⁺) contributed around 12%, 43% and 16% of the overall PM_2.5 mass during pre-haze, haze and post-haze periods, respectively. During haze periods, the predominant source identified using PMF was secondary inorganic aerosol (SIA) and biomass burning where the NAME simulations indicate the importance of fires in Sumatra, Indonesia. The main source during pre-haze and post-haze were mix SIA and road dust as well as mineral dust, respectively. The highest non-carcinogenic health risk during haze episode was estimated among the infant group (HI=1.06) while the highest carcinogenic health risk was estimated among the adult group (2.27×10^-5).

The influence of odd-even car trial on fine and coarse particles in Delhi

The odd-even car trial scheme, which reduced car traffic between 08.00 and 20.00 h daily, was applied from 1 to 15 January 2016 (winter scheme, WS) and 15-30 April 2016 (summer scheme, SS). The daily average PM_2.5 and PM₁₀ exceeded national standards, with highest concentrations (313 μg m^-3 and 639 μg m^-3, respectively) during winter and lowest (53 μg m^-3 and 130 μg m^-3) during the monsoon (June-August). PM concentrations during the trials can be interpreted either as reduced or increased, depending on the periods used for comparison purposes. For example, hourly average net PM_2.5 and PM₁₀ (after subtracting the baseline concentrations) reduced by up to 74% during the majority (after 1100 h) of trial hours compared with the corresponding hours during the previous year. Conversely, daily average PM_2.5 and PM₁₀ were higher by up to 3-times during the trial periods when compared with the pre-trial days. A careful analysis of the data shows that the trials generated cleaner air for certain hours of the day but the persistence of overnight emissions from heavy goods vehicles into the morning odd-even hours (0800-1100 h) made them probably ineffective at this time. Any further trial will need to be planned very carefully if an effect due to traffic alone is to be differentiated from the larger effect caused by changes in meteorology and especially wind direction.