Experimental investigation of vegetative environment buffers in reducing particulate matters emitted from ventilated poultry house

Scientists have effectively proved that vegetative environment buffers (VEBs) can be used for reducing dust emissions from livestock buildings, but they have seen fewer tests in poultry farms. A field research was conducted to assess the effectiveness of VEBs on reducing downwind transport of particulate matter (PM) from a ventilated poultry house in Changchun. Five plant species transferred from local area were used to establish five diverse VEBs and separately installed outside of the ventilation fans in summer 2017. The five plant species were Winged Euonymus (WE), Malus Spectabilis (MS), Padus Maackii (PAA), Acer Saccharum Marsh (ASM), and Padus Virginiana “Red Select Shrub” (PV_RSS). The mass concentrations of PM_2.5 and PM₁₀ (particulate matter with an aerodynamic diameter of 2.5 μm and 10 μm or less, respectively) were monitored at downwind and upwind sampling locations around the VEB. The results showed that with the presenting of VEBs, the particle concentrations at the downwind sampling point were significantly reduced compared with that at the upwind sampling point (p < 0.05). Specifically, compared to the control test without VEB, the VEB with PV_RSS had the best PM concentration reduction rate (CRR) of 47.24%±4.33% and 41.13%±5.83% for PM_2.5 and PM₁₀, respectively. The rough surface of plant leaves may help intercept more PM, though it was also affected by other factors (such as the blade angle, the interaction with wind) needed to be further investigated. The VEB with PV_RSS, which presented the best capacity of CRR, selectively intercepted PM, mainly related to the elements of N, Na, Mg, P, S, and Cl.

Implications: Five plant species, including WE, PAA, MS, ASM, and PV_RSS, were evaluated as VEBs to mitigate particulate emissions from outside of a ventilated poultry house in Changchun. They all significantly reduced particulate matter emissions. However, the PV_RSS presented the best capability of trapping fine and coarse particles: PM_2.5 and PM₁₀, respectively, while the PAA was the worst one. The microstructure of leaves affected particle deposition and remaining on the leaves, and PV_RSS selectively intercepted particulate matter mainly related to certain elements.     

Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis

This study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM₁₀), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM_2.5) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM_2.5 (r² = 0.62), and the two-variable (AOD-PM_2.5) model predicted PM_2.5 (r² = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM_2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM_2.5. For the relevant period in 2008, in Roda, VA, the predicted PM_2.5 mass concentration is 9.11 ± 5.16 μg m^-3 (mean ± 1SD).

Implications: This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or “hollows,” where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.     

Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates

Given the significance of mining as a source of particulates, accurate characterization of emissions is important for the development of appropriate emission estimation techniques for use in modeling predictions and to inform regulatory decisions. The currently available emission estimation methods for Australian open-cut coal mines relate primarily to total suspended particulates and PM₁₀ (particulate matter with an aerodynamic diameter <10 μm), and limited data are available relating to the PM_2.5 (<2.5 μm) size fraction. To provide an initial analysis of the appropriateness of the currently available emission estimation techniques, this paper presents results of sampling completed at three open-cut coal mines in Australia. The monitoring data demonstrate that the particulate size fraction varies for different mining activities, and that the region in which the mine is located influences the characteristics of the particulates emitted to the atmosphere. The proportion of fine particulates in the sample increased with distance from the source, with the coarse fraction being a more significant proportion of total suspended particulates close to the source of emissions. In terms of particulate composition, the results demonstrate that the particulate emissions are predominantly sourced from naturally occurring geological material, and coal comprises less than 13% of the overall emissions. The size fractionation exhibited by the sampling data sets is similar to that adopted in current Australian emission estimation methods but differs from the size fractionation presented in the U.S. Environmental Protection Agency methodology. Development of region-specific emission estimation techniques for PM₁₀ and PM_2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions.

Implications: Development of region-specific emission estimation techniques for PM₁₀ and PM_2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Comprehensive air quality monitoring was undertaken, and corresponding recommendations were provided.     

Effect modification of ambient particle mortality by radon: A time series analysis in 108 U.S. cities

Numerous studies have reported a positive association between ambient fine particles and daily mortality, but little is known about the particle properties or environmental factors that may contribute to these effects. This study assessed potential modification of radon on PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm)-associated daily mortality in 108 U.S. cities using a two-stage statistical approach. First, city- and season-specific PM_2.5 mortality risks were estimated using over-dispersed Poisson regression models. These PM_2.5 effect estimates were then regressed against mean city-level residential radon concentrations to estimate overall PM_2.5 effects and potential modification by radon. Radon exposure estimates based on measured short-term basement concentrations and modeled long-term living-area concentrations were both assessed. Exposure to PM_2.5 was associated with total, cardiovascular, and respiratory mortality in both the spring and the fall. In addition, higher mean city-level radon concentrations increased PM_2.5-associated mortality in the spring and fall. For example, a 10 µg/m³ increase in PM_2.5 in the spring at the 10th percentile of city-averaged short-term radon concentrations (21.1 Bq/m³) was associated with a 1.92% increase in total mortality (95% CI: 1.29, 2.55), whereas the same PM_2.5 exposure at the 90th radon percentile (234.2 Bq/m³) was associated with a 3.73% increase in total mortality (95% CI: 2.87, 4.59). Results were robust to adjustment for spatial confounders, including average planetary boundary height, population age, percent poverty and tobacco use. While additional research is necessary, this study suggests that radon enhances PM_2.5 mortality. This is of significant regulatory importance, as effective regulation should consider the increased risk for particle mortality in cities with higher radon levels.

Implications: In this large national study, city-averaged indoor radon concentration was a significant effect modifier of PM_2.5-associated total, cardiovascular, and respiratory mortality risk in the spring and fall. These results suggest that radon may enhance PM_2.5-associated mortality. In addition, local radon concentrations partially explain the significant variability in PM_2.5 effect estimates across U.S. cities, noted in this and previous studies. Although the concept of PM as a vector for radon progeny is feasible, additional research is needed on the noncancer health effects of radon and its potential interaction with PM. Future air quality regulations may need to consider the increased risk for particle mortality in cities with higher radon levels.     

Temporal variations of fine and coarse particulate matter sources in Jeddah,Saudi Arabia

This study provides the first comprehensive analysis of the seasonal variations and weekday/weekend differences in fine (aerodynamic diameter <2.5 μm; PM_2.5) and coarse (aerodynamic diameter 2.5–10 μm; PM_2.5–10) particulate matter mass concentrations, elemental constituents, and potential source origins in Jeddah, Saudi Arabia. Air quality samples were collected over 1 yr, from June 2011 to May 2012 at a frequency of three times per week, and analyzed. The average mass concentrations of PM_2.5 (21.9 μg/m³) and PM₁₀ (107.8 μg/m³) during the sampling period exceeded the recommended annual average levels by the World Health Organization (WHO) for PM_2.5 (10 μg/m³) and PM₁₀ (20 μg/m³), respectively. Similar to other Middle Eastern locales, PM_2.5–10 is the prevailing mass component of atmospheric particulate matter at Jeddah, accounting for approximately 80% of the PM₁₀ mass. Considerations of enrichment factors, absolute principal component analysis (APCA), concentration roses, and backward trajectories identified the following source categories for both PM_2.5 and PM_2.5–10: (1) soil/road dust, (2) incineration, and (3) traffic; and for PM_2.5 only, (4) residual oil burning. Soil/road dust accounted for a major portion of both the PM_2.5 (27%) and PM_2.5–10 (77%) mass, and the largest source contributor for PM_2.5 was from residual oil burning (63%). Temporal variations of PM_2.5–10 and PM_2.5 were observed, with the elevated concentration levels observed for mass during the spring (due to increased dust storm frequency) and on weekdays (due to increased traffic). The predominant role of windblown soil and road dust in both the PM_2.5 and PM_2.5–10 masses in this city may have implications regarding the toxicity of these particles versus those in the Western world where most PM health assessments have been made in the past. These results support the need for region-specific epidemiological investigations to be conducted and considered in future PM standard setting.

Implications: Temporal variations of fine and coarse PM mass, elemental constituents, and sources were examined in Jeddah, Saudi Arabia, for the first time. The main source of PM_2.5–10 is natural windblown soil and road dust, whereas the predominant source of PM_2.5 is residual oil burning, generated from the port and oil refinery located west of the air sampler, suggesting that targeted emission controls could significantly improve the air quality in the city. The compositional differences point to a need for health effect studies to be conducted in this region, so as to directly assess the applicability of the existing guidelines to the Middle East air pollution.     

Synthesis of Harvard Environmental Protection Agency (EPA) Center studies on traffic-related particulate pollution and cardiovascular outcomes in the Greater Boston Area

The association between particulate pollution and cardiovascular morbidity and mortality is well established. While the cardiovascular effects of nationally regulated criteria pollutants (e.g., fine particulate matter [PM_2.5] and nitrogen dioxide) have been well documented, there are fewer studies on particulate pollutants that are more specific for traffic, such as black carbon (BC) and particle number (PN). In this paper, we synthesized studies conducted in the Greater Boston Area on cardiovascular health effects of traffic exposure, specifically defined by BC or PN exposure or proximity to major roadways. Large cohort studies demonstrate that exposure to traffic-related particles adversely affect cardiac autonomic function, increase systemic cytokine-mediated inflammation and pro-thrombotic activity, and elevate the risk of hypertension and ischemic stroke. Key patterns emerged when directly comparing studies with overlapping exposure metrics and population cohorts. Most notably, cardiovascular risk estimates of PN and BC exposures were larger in magnitude or more often statistically significant compared to those of PM_2.5 exposures. Across multiple exposure metrics (e.g., short-term vs. long-term; observed vs. modeled) and different population cohorts (e.g., elderly, individuals with co-morbidities, young healthy individuals), there is compelling evidence that BC and PN represent traffic-related particles that are especially harmful to cardiovascular health. Further research is needed to validate these findings in other geographic locations, characterize exposure errors associated with using monitored and modeled traffic pollutant levels, and elucidate pathophysiological mechanisms underlying the cardiovascular effects of traffic-related particulate pollutants.

Implications: Traffic emissions are an important source of particles harmful to cardiovascular health. Traffic-related particles, specifically BC and PN, adversely affect cardiac autonomic function, increase systemic inflammation and thrombotic activity, elevate BP, and increase the risk of ischemic stroke. There is evidence that BC and PN are associated with greater cardiovascular risk compared to PM_2.5. Further research is needed to elucidate other health effects of traffic-related particles and assess the feasibility of regulating BC and PN or their regional and local sources.     

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial,California, USA

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R² for converted hourly averaged Dylos mass measurements versus a PM_2.5 BAM was 0.79 and that versus a PM₁₀ BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM_2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R² = 0.35–0.81).

Implications: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.     

Design and characterization of human exposure to generated sulfate and soot particles in a pilot chamber study

A number of literatures have documented adverse health effects of exposure to fine particulate matter (PM_2.5), and secondary sulfate aerosol and black carbon may contribute to health impacts of PM_2.5 exposure. We designed an exposure system to generate sulfate and traffic soot particles, and assessed the feasibility of using it for human exposure assessment in a pilot human exposure study. In the designed exposure system, average mass concentrations of generated sulfate and soot particles were 74.19μg/m³ and 11.54μg/m³ in the chamber and did not vary significantly during two-hour human exposure sessions. The size ranges of generated sulfate were largely between 20 to 200 nm, whereas those of generated soot particles were in the size ranges of 50 to 200nm. Following two-hour exposure to generated sulfate and soot particles, we observed significant increases in fractional exhaled NO (FeNO) in young and health subjects. Building on established human exposure system and health response follow-up methods, future full-scale studies focusing on the effects of mixed particulates and individual PM_2.5 components would provide data in understanding the underpinning cardio-respiratory outcomes in relation to air pollution mixture exposure.

Implications: Controlled exposure is a useful design to measure the biological responses repeatedly following particulate exposures of target components and set exposure at target levels of health concerns. Our study provides rational and establishes method for future full-scale studies to focus on examining the effects of mixed particulates and individual PM_2.5 components.     

Improving the correlations of ambient tapered element oscillating microbalance PM2.5 data and SHARP 5030 Federal Equivalent Method in Ontario: A multiple linear regression analysis

Tapered element oscillating microbalances equipped with sample equilibration system (TEOM-SES) used by the province of Ontario for the ambient monitoring of PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) in its air quality index (AQI) network were collocated with the Synchronized Hybrid Ambient Real-time Particulate monitor (SHARP 5030) at two monitoring sites for a period spanning approximately 2 years to determine the similarities and differences between the measurement outputs of both instrumental systems. Due mainly to mass loss observed with the TEOM-SES in cooler months, the province has recently switched its PM_2.5 instrumentation at all stations in its monitoring network from the TEOM-SES to the SHARP 5030, which has the U.S. Environmental Protection Agency (EPA) Federal Equivalent Method (FEM) Class III designation. Thus, it has become imperative to develop corrections for historical and future TEOM measurements for the purpose of making them more agreeable to the new FEM method. This work details the authors’ multiple linear regression analyses (MLRAs) of particulate matter data from both instrumental monitors, with the inclusion of operational parameters of physicochemical relevance for both cases of transformations of historical TEOM and TEOM measurements to be made in the future. For historical TEOM data, it was observed that the transformations only benefited winter and fall months. Furthermore, comparisons of the transformed historical TEOM data with PM_2.5 concentrations determined from the Federal Reference Method (FRM) sampler at seven locations within the province showed marked improvements over the observed TEOM-FRM comparisons.

Implications:This work provides a path to correcting the historically observed underreporting of particulate mass in winter and fall in Ontario by making the TEOM-based continuous data resemble the new FEM outputs (in this case, more SHARP-like). It is possible that the transformation of mainly winter TEOM data as detailed in this work may potentially lead to revisions in historical annual composite mean PM_2.5 concentrations and total annual number of days PM_2.5 exceeded the Canada-wide Standard (CWS) metric across the province.     

Feasibility of using low-cost portable particle monitors for measurement of fine and coarse particulate matter in urban ambient air

Exposure to ambient particulate matter (PM) is known as a significant risk factor for mortality and morbidity due to cardiorespiratory causes. Owing to increased interest in assessing personal and community exposures to PM, we evaluated the feasibility of employing a low-cost portable direct-reading instrument for measurement of ambient air PM exposure. A Dylos DC 1700 PM sensor was collocated with a Grimm 11-R in an urban residential area of Houston Texas. The 1-min averages of particle number concentrations for sizes between 0.5 and 2.5 µm (small size) and sizes larger than 2.5 µm (large size) from a DC 1700 were compared with the 1-min averages of PM_2.5 (aerodynamic size less than 2.5 µm) and coarse PM (aerodynamic size between 2.5 and 10 µm) concentrations from a Grimm 11-R. We used a linear regression equation to convert DC 1700 number concentrations to mass concentrations, utilizing measurements from the Grimm 11-R. The estimated average DC 1700 PM_2.5 concentration (13.2 ± 13.7 µg/m³) was similar to the average measured Grimm 11-R PM_2.5 concentration (11.3 ± 15.1 µg/m³). The overall correlation (r²) for PM_2.5 between the DC 1700 and Grimm 11-R was 0.778. The estimated average coarse PM concentration from the DC 1700 (5.6 ± 12.1 µg/m³) was also similar to that measured with the Grimm 11-R (4.8 ± 16.5 µg/m³) with an r² of 0.481. The effects of relative humidity and particle size on the association between the DC 1700 and the Grimm 11-R results were also examined. The calculated PM mass concentrations from the DC 1700 were close to those measured with the Grimm 11-R when relative humidity was less than 60% for both PM_2.5 and coarse PM. Particle size distribution was more important for the association of coarse PM between the DC 1700 and Grimm 11-R than it was for PM_2.5.

Implications: The performance of a low-cost particulate matter (PM) sensor was evaluated in an urban residential area. Both PM_2.5 and coarse PM (PM_10-2.5) mass concentrations were estimated using a DC1700 PM sensor. The calculated PM mass concentrations from the number concentrations of DC 1700 were close to those measured with the Grimm 11-R when relative humidity was less than 60% for both PM_2.5 and coarse PM. Particle size distribution was more important for the association of coarse PM between the DC 1700 and Grimm 11-R than it was for PM_2.5.     

Investigation and modeling of the residential infiltration of fine particulate matter in Beijing,China

The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM_2.5) and to develop models to predict PM_2.5 Finf in Beijing. Eighty-eight paired indoor–outdoor PM_2.5 samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM_2.5 were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM_2.5 Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM_2.5 Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM_2.5 Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM_2.5 Finf into large epidemiology studies, thereby reducing exposure misclassification.

Implications: Failure to consider the differences between indoor and outdoor PM_2.5 may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM_2.5 infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM_2.5 infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM_2.5 of outdoor origin in epidemiological studies. Localized predictive models for PM_2.5 infiltration factor were also developed.     

Concentrations of aliphatic and polycyclic aromatic hydrocarbons in ambient PM2.5 and PM10 particulates in Doha,Qatar

Organic carbon (OC), elemental carbon (EC), and 90 organic compounds (36 polycyclic aromatic hydrocarbons [PAHs], 25 n-alkane homologues, 17 hopanes, and 12 steranes) were concurrently quantified in atmospheric particulate matter of PM_2.5 and PM₁₀. The 24-hr PM samples were collected using Harvard Impactors at a suburban site in Doha, Qatar, from May to December 2015. The mass concentrations (mean ± standard deviation) of PM_2.5 and PM₁₀ were 40 ± 15 and 145 ± 70 µg m^?3, respectively, exceeding the World Health Organization (WHO) air quality guidelines. Coarse particles comprised 70% of PM₁₀. Total carbonaceous contents accounted for 14% of PM_2.5 and 10% of PM₁₀ particulate mass. The major fraction (90%) of EC was associated with the PM_2.5. In contrast, 70% of OC content was found in the PM_2.5–10 fraction. The secondary OC accounted for 60–68% of the total OC in both PM fractions, indicating photochemical conversions of organics are much active in the area due to higher air temperatures and solar radiations. Among the studied compounds, n-alkanes were the most abundant group, followed by PAHs, hopanes, and steranes. n-Alkanes from C₂₅ to C₃₅ prevailed with a predominance of odd carbon numbered congeners (C₂₇–C₃₁). High-molecular-weight PAHs (5–6 rings) also prevailed, within their class, with benzo[b + j]fluoranthene (Bb + jF) being the dominant member. PAHs were mainly (80%) associated with the PM_2.5 fraction. Local vehicular and fugitive emissions were predominant during low-speed southeasterly winds from urban areas, while remote petrogenic/biogenic emissions were particularly significant under prevailing northwesterly wind conditions.

Implications: An unprecedented study in Qatar established concentration profiles of EC, OC, and 90 organic compounds in PM_2.5 and PM₁₀. Multiple tracer organic compounds for each source can be used for convincing source apportionment. Particle concentrations exceeded WHO air quality guidelines for 82–96% of the time, revealing a severe problem of atmospheric PM in Doha. Dominance of EC and PAHs in fine particles signifies contributions from combustion sources. Dependence of pollutants concentrations on wind speed and direction suggests their significant temporal and spatial variability, indicating opportunities for improving the air quality by identifying sources of airborne contaminants.     

Recent spatial gradients and time trends in Dhaka,Bangladesh, air pollution and their human health implications

Dhaka, the capital of Bangladesh, is among the most polluted cities in the world. This research evaluates seasonal patterns, day-of-week patterns, spatial gradients, and trends in PM_2.5 (<2.5 µm in aerodynamic diameter), PM₁₀ (<10 µm in aerodynamic diameter), and gaseous pollutants concentrations (SO₂, NO₂, CO, and O₃) monitored in Dhaka from 2013 to 2017. It expands on past work by considering multiple monitoring sites and air pollutants. Except for ozone, the average concentrations of these pollutants showed strong seasonal variation, with maximum during winter and minimum during monsoon, with the pollution concentration of PM_2.5 and PM₁₀ being roughly five- to sixfold higher during winter versus monsoon. Our comparisons of the pollutant concentrations with Bangladesh NAAQS and U.S. NAAQS limits analysis indicate particulate matter (PM_2.5 and PM₁₀) as the air pollutants of greatest concern, as they frequently exceeded the Bangladesh NAAQS and U.S. NAAQS, especially during nonmonsoon time. In contrast, gaseous pollutants reported far fewer exceedances throughout the study period. During the study period, the highest number of exceedances of NAAQS limits in Dhaka City (Darus-Salam site) were found for PM_2.5 (72% of total study days), followed by PM₁₀ (40% of total study days), O₃ (1.7% of total study days), SO₂ (0.38% of total study days), and CO (0.25% of total study days). The trend analyses results showed statistically significant positive slopes over time for SO₂ (5.6 ppb yr^?1, 95% confidence interval [CI]: 0.7, 10.5) and CO (0.32 ppm yr^?1, 95% CI: 0.01, 0.56), which suggest increase in brick kilns operation and high-sulfur diesel use. Though statistically nonsignificant annual decreasing slopes for PM_2.5 (?4.6 µg/m³ yr^?1, 95% CI: ?12.7, 3.6) and PM₁₀ (?2.7 µg/m³ yr^?1, 95% CI: ?7.9, 2.5) were observed during this study period, the PM_2.5 concentration is still too high (~ 82.0 µg/m³) and can cause severe impact on human health.

Implications: This study revealed key insights into air quality challenges across Dhaka, Bangladesh, indicating particulate matter (PM) as Dhaka’s most serious air pollutant threat to human health. The results of these analyses indicate that there is a need for immediate further investigations, and action based on those investigations, including the conduct local epidemiological PM exposure-human health effects studies for this city, in order to determine the most public health effective interventions.     

Influence of sulfur dioxide-related interactions on PM2.5 formation in iron ore sintering

The formation of PM_2.5 (aerosol particulate matter less than 2.5 µm in aerodynamic diameter) in association with SO₂ emission during sintering process has been studied by dividing the whole sintering process into six typical sampling stages. A low-pressure cascade impactor was used to collect PM_2.5 by automatically segregating particulates into six sizes. It was found that strong correlation existed between the emission properties of PM_2.5 and SO₂. Wet mixture layer (overwetted layer and raw mixture layer) had the function to simultaneously capture SO₂ and PM_2.5 during the early sintering stages, and released them back into flue gas mainly in the flue gas temperature-rising period. CaSO₄ crystals constituted the main SO₂-related PM_2.5 during the disappearing process of overwetted layer, which was able to form perfect individual crystals or to form particles with complex chemical compositions. Besides the existence of individual CaSO₄ crystals, mixed crystals of K₂SO₄-CaSO₄ in PM_2.5 were also found during the first half of the temperature-rising period of flue gas. The interaction between fine-grained Ca-based fluxes, potassium vapors, and SO₂ was the potential source of SO₂-related PM_2.5.

Implications: The emission property of PM_2.5 and SO₂ throughout the sintering process exhibited well similarity. This phenomenon tightened the relationship between the formation of PM_2.5 and the emission of SO₂. Through revealing the properties of SO₂-related PM_2.5 during sintering process, the potential interaction between fine-grained Ca-based fluxes, potassium vapors, and SO₂ was found to be the source of SO₂-related PM_2.5. This information can serve as the guidance to develop efficient techniques to control the formation and emission of PM_2.5 in practical sintering plants.     

Outdoor tobacco smoke exposure at the perimeter of a tobacco-free university

There are few studies measuring exposure to outdoor tobacco smoke (OTS). Tobacco users often gather at the boundaries of tobacco-free campuses, resulting in unintended consequences. The objective of this study was to measure exposure levels from OTS on sidewalks bordering a tobacco-free university campus. Data were collected while walking along a sidewalk adjacent to a medium traffic road between May and August 2011. Monitoring occurred during “background,” “stop,” and “walk-through” conditions at and near hot spot area to measure fine particulate matter (<2.5 μm; PM_2.5) from OTS using a portable aerosol monitor. The average PM_2.5 levels during stop and walk-through conditions were significantly higher than during background conditions. PM_2.5 peak occurrence rate and magnitude of peak concentration were significantly different depending on smoking occurrence. The peak occurrence rate during the stop condition was 10.4 times higher than during the background condition, and 3.1 times higher than during the walk-through condition. Average peak PM_2.5 concentrations during the stop condition were 48.7% higher than during the background condition. In conclusion, individuals could be exposed to high levels of PM_2.5 when stopping or even passing by smokers outdoors at the perimeter of tobacco-free campuses. The design and implementation of tobacco-free campus policies need to take into account the unintended consequences of OTS exposure at the boundaries.

Implications:In this study, outdoor tobacco smoke (OTS) exposure was measured at the perimeter of tobacco-free campus. OTS exposure could be determined by peak analysis. Peak occurrence rate and peak concentration for OTS exposure were identified by using peak analysis. People could be exposed to high levels of PM_2.5 when standing or even passing by smokers at the perimeter of tobacco-free campus. OTS exposure measurement in other outdoor locations with smokers is needed to support outdoor smoking regulation.     

The impact of photovoltaic (PV) installations on downwind particulate matter concentrations: Results from field observations at a 550-MWAC utility-scale PV plant

With utility-scale photovoltaic (PV) projects increasingly developed in dry and dust-prone geographies with high solar insolation, there is a critical need to analyze the impacts of PV installations on the resulting particulate matter (PM) concentrations, which have environmental and health impacts. This study is the first to quantify the impact of a utility-scale PV plant on PM concentrations downwind of the project site. Background, construction, and post-construction PM_2.5 and PM₁₀ (PM with aerodynamic diameters <2.5 and <10 μm, respectively) concentration data were collected from four beta attenuation monitor (BAM) stations over 3 yr. Based on these data, the authors evaluate the hypothesis that PM emissions from land occupied by a utility-scale PV installation are reduced after project construction through a wind-shielding effect. The results show that the (1) confidence intervals of the mean PM concentrations during construction overlap with or are lower than background concentrations for three of the four BAM stations; and (2) post-construction PM_2.5 and PM₁₀ concentrations downwind of the PV installation are significantly lower than the background concentrations at three of the four BAM stations. At the fourth BAM station, downwind post-construction PM_2.5 and PM₁₀ concentrations increased marginally by 5.7% and 2.6% of the 24-hr ambient air quality standards defined by the U.S. Environmental Protection Agency, respectively, when compared with background concentrations, with the PM_2.5 increase being statistically insignificant. This increase may be due to vehicular emissions from an access road near the southwest corner of the site or a drainage berm near the south station. The findings demonstrate the overall environmental benefit of downwind PM emission abatement from a utility-scale PV installation in desert conditions due to wind shielding. With PM emission reductions observed within 10 months of completion of construction, post-construction monitoring of downwind PM levels may be reduced to a 1-yr period for other projects with similar soil and weather conditions.

Implications: This study is the first to analyze impact of a utility photovoltaic (PV) project on downwind particulate matter (PM) concentration in desert conditions. The PM data were collected at four beta attenuation monitor stations over a 3-yr period. The post-construction PM concentrations are lower than background concentrations at three of four stations, therefore supporting the hypothesis of post-construction wind shielding from PV installations. With PM emission reductions observed within 10 months of completion of construction, postconstruction monitoring of downwind PM levels may be reduced to a 1-yr period for other PV projects with similar soil and weather conditions.     

Purification characteristics of fine particulate matter treated by a self-flushing wet electrostatic precipitator equipped with a flexible electrode

A self-flushing wet electrostatic precipitator was developed to investigate the removal performance for fine particles. Flexible material (polypropylene, 840A) and carbon steel in the form of a spiked band were adopted as the collection plate and discharge electrode, respectively. The particle concentration, morphology, and trace-element content were measured by electric low-pressure impactor, scanning electron microscope, and energy-dispersive x-ray spectroscopy, respectively, before and after the electrostatic precipitator. With increasing gas velocity, the collection efficiency of fine particles (up to 0.8 μm in diameter) increased, while it decreased for particles with diameters larger than 0.8 μm. Increasing the dust inlet concentration increased the collection efficiency up to a point, from which it then declined gradually with further increases in the inlet concentration. The particulate matter after the wet electrostatic precipitator showed different degrees of agglomeration. The collection efficiency of trace elements within PM₁₀ was less than that of the PM₁₀ itself. Notably, the water consumption in the current setup was significantly lower than for other treatment processes of comparable collection efficiencies.

Implications: Wet electrostatic precipitators, as fine filtration equipment, were generally applicable to coal-fired plants to reduce PM_2.5 emissions in China. However, high energy consumption and unstable operation, such as water usage and spray washing directly in the electric field, seriously restricted the further development. The utilization of self-flushing wet electrostatic precipitator can solve these problems to some extent.     

Public health and components of particulate matter: The changing assessment of black carbon

In 2012, the WHO classified diesel emissions as carcinogenic, and its European branch suggested creating a public health standard for airborne black carbon (BC). In 2011, EU researchers found that life expectancy could be extended four to nine times by reducing a unit of BC, vs reducing a unit of PM_2.5. Only recently could such determinations be made. Steady improvements in research methodologies now enable such judgments.

In this Critical Review, we survey epidemiological and toxicological literature regarding carbonaceous combustion emissions, as research methodologies improved over time. Initially, we focus on studies of BC, diesel, and traffic emissions in the Western countries (where daily urban BC emissions are mainly from diesels). We examine effects of other carbonaceous emissions, e.g., residential burning of biomass and coal without controls, mainly in developing countries.

Throughout the 1990s, air pollution epidemiology studies rarely included species not routinely monitored. As additional PM_2.5. chemical species, including carbonaceous species, became more widely available after 1999, they were gradually included in epidemiological studies. Pollutant species concentrations which more accurately reflected subject exposure also improved models.

Natural “interventions” - reductions in emissions concurrent with fuel changes or increased combustion efficiency; introduction of ventilation in highway tunnels; implementation of electronic toll payment systems – demonstrated health benefits of reducing specific carbon emissions. Toxicology studies provided plausible biological mechanisms by which different PM species, e.g., carbonaceous species, may cause harm, aiding interpretation of epidemiological studies.

Our review finds that BC from various sources appears to be causally involved in all-cause, lung cancer, and cardiovascular mortality, morbidity, and perhaps adverse birth and nervous system effects. We recommend that the U.S. EPA rubric for judging possible causality of PM_2.5. mass concentrations, be used to assess which PM_2.5. species are most harmful to public health.

Implications: Black carbon (BC) and correlated co-emissions appear causally related with all-cause, cardiovascular, and lung cancer mortality, and perhaps with adverse birth outcomes and central nervous system effects. Such findings are recent, since widespread monitoring for BC is also recent. Helpful epidemiological advances (using many health relevant PM_2.5 species in models; using better measurements of subject exposure) have also occurred. “Natural intervention” studies also demonstrate harm from partly combusted carbonaceous emissions. Toxicology studies consistently find biological mechanisms explaining how such emissions can cause these adverse outcomes. A consistent mechanism for judging causality for different PM_2.5 species is suggested.

A list of acronyms will be found at the end of the article.     

Urban energy environment efficiency in China: Based on dynamic meta-frontier slack-based measures

Rapid economic growth in China has resulted in a significant increase in particulate matter (PM_2.5) and sulfur dioxide (SO₂), the reduction of which has become a primary government focus. However, as the energy consumption and air pollutant emissions in Chinese cities have very significant regional characteristics, individual governance measures are necessary. This study used 2013 to 2016 energy consumption data from 31 Chinese cities to evaluate the dynamic efficiency of the urban environments. Labor, fixed assets, and energy consumption were taken as the inputs, gross domestic product (GDP) was taken as the output, and particulate matter (PM_2.5) and sulfur dioxide (SO₂) were taken as the carry-over variable indicators. Using a meta-frontier dynamic DEA model, the 31 cities were classified into high-income and upper-middle-income cities, the overall 2013–2016 energy consumption and air pollutant efficiency scores were analyzed, and improvements and changes were recommended to increase the efficiencies. Large differences were found in the energy consumption and air pollution emissions efficiency scores and the needed improvements, with the hig-income cities performing better overall than the upper-middle-income cities. While there have been some significant improvements in SO₂ emissions, PM_2.5 improvements have been far slower. Therefore, in most cities, more control measures are needed to control PM_2.5 emissions. However, in addition to improving PM_2.5 in the upper-middle-income cities, SO₂treatments are also needed.

Implications: There are big differences in the expectation of improvement of the two pollutants in all cities. In many Western cities, the expectation of PM2.5 improvement in the past years has not been reduced, but has been expanding. This shows that the central government has unified the air pollution control policies and the existing air pollution control measures formulated and implemented by the local governments.     

Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan,China

Particulate matter with aerodynamic diameter below 10 μm (PM₁₀) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM₁₀ concentrations. It was evaluated by experiments using a 10-year data set of daily PM₁₀ concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM₁₀ concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM₁₀ concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM₁₀ concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM₁₀ concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM₁₀ forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction.

Implications: Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM₁₀ time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM₁₀ time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM₁₀ time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM₁₀ forecasting field.