Secondary PM2 Research Articles

Meteorological characteristics within boundary layer and its influence on PM2.5 pollution in six cities of North China based on WRF-Chem

North China is recognized as one of the region with most severe air pollution in China, and the PM2.5 in six major cities (Beijing, Tianjin, Shijiazhuang, Taiyuan, Jinan and Zhengzhou) of this region was measured to be 108.7–186.0 μg m−3 in January 2017 and 39.2–56.3 μg m−3 in July 2017. This study conducts the meteorological and chemical simulations based on WRF-Chem, and explores the impacts of meteorology and emissions on PM2.5 (particulate matter with aerodynamic diameters ≤ 2.5 μm) pollutions. Simulations show a noticeable seasonal variation in planetary boundary layer height (PBLH) and relative humidity (RH), but a similar trend in wind speed (WS). Compared to PM2.5 good condition (≤75 μg m−3), daily PBLH decreased by 7.6–39.6% in January and 9.2–44.1% in July during PM2.5 polluted condition (75~150 μg m−3), and by 22.3–51.2% in January during severely polluted condition (≥150 μg m−3). PBLH is thought of as the key factor for PM2.5. Then, the synergy between PBLH and other meteorological factors are studied. For a 100 m failing in PBLH, the decrease in surface WS reaches 0.2–0.8 m s−1 0.1–0.3 m s−1 in January and July. It means a synergic effect of unfavorable horizontal and vertical dispersions, more adversely in January than in July. While, RH presents an increasing trend with PBLH falling, about +2.1~+3.2% in January and +2.7~+4.6% in July per 100 m falling in PBLH. Considering the improvement of high RH in heterogeneous chemistry to form the secondary PM2.5, we believe that the disadvantaged dispersion condition is always accompanied by enhanced secondary PM2.5 formation chemistry. Then, we summarize the average simulated PM2.5 under various combinations of PBLH, WS and RH, and find that under the same meteorological combination, PM2.5 in January was about 1.7–3.6 times that of July, which could be explained by the more emissions of PM2.5 and its precursors in January. Finally, we determine the unfavorable meteorology condition based on current regional emissions and China's standard (hourly PM2.5<75 μg m−3), low PBLH (<600 m) or middle-high RH (>40%) for January, high RH (>60%) & low-middle WS (<4 m·s-1), or middle RH (40~60%) & low PBLH (<400 m) for July. These unfavorable meteorological conditions accounted for 75.8% in January and 47.8% in July. Therefore, it is still necessary to continuously reduce anthropogenic emissions in this region for attainment of China's PM2.5 standards.

Air Pollution Exposure and Lung Function Until Age 16 Years: The PIAMA Birth Cohort Study

Evidence for the effects of air pollution exposure on lung function growth into adolescence is scarce. Therefore, the purpose of this study was to investigate the associations of air pollution exposure from birth with lung function growth from ages 8 to 16 and lung function at age 16. Potential confounders (sex and asthma diagnosis) were also explored.The study was performed within the Dutch population-based Prevention and Incidence of Asthma and Mite Allergy birth cohort. From the 3963 pregnant women initially enrolled, 915 children (mean age: 16.3 years) had sufficient longitudinal data for this study.Both longitudinal (n = 915) and cross-sectional (n = 721) analyses were conducted. Researchers estimated residential concentrations of nitrogen dioxide, “soot,” and particulate matter (PMx, where x is the 50% cutoff aerodynamic diameter in μm) with diameters of <2.5 μm (PM2.5), <10 μm (PM10), and 2.5 to 10 μm (PMcoarse) during the preschool, primary school, and secondary school time windows by land use regression models. Associations with (growth in) forced expiratory volume in 1 second (FEV1) and forced vital capacity were analyzed by linear (mixed effects) regression.Greater air pollution exposure was associated with reduced FEV1 growth (eg, adjusted difference: −0.26% [95% confidence interval: −0.49 to −0.03%] per interquartile range increase in secondary school PM2.5) and lower FEV1 (adjusted difference: −2.36% [95% confidence interval: −3.76 to −0.94%]) but was not adversely associated with forced vital capacity. Associations with FEV1 were stronger in boys than girls and were not modified by asthma status.Greater air pollution exposure may lead to increased airway obstruction but not reduced lung volume in adolescence.This study supports previous longitudinal studies that have reported an association between air pollution exposure and reduced lung function growth. The association between lower FEV1 and PM2.5 exposure during the secondary school time window suggests that exposure during this time frame is critical. This study was limited by a lack of racial diversity, which could create differing results because of epigenetic changes. Future longitudinal studies should address this issue.

On the environmental effectiveness analysis of energy policies: A case study of air pollution in the megacity of Tehran

The present study compared different approaches to assessing the environmental cost-effectiveness of energy policy scenarios. As a case study, the megacity of Tehran in Iran was studied. A key policy challenge in this city is to curb high concentrations of PM2.5 and mitigate the associated adverse impacts. The results demonstrated that in the business as usual case, the spatially averaged primary and secondary PM2.5 concentration in Tehran will increase by 30% in the 2010–2030 period. Adopting certain planned policy scenarios and the corresponding pollutant concentration reductions in Tehran shows that although most of the emission comes from industrial activities around the city, the distribution of the transportation emission sources may play the most effective role in decreasing pollution levels in transport-related energy policies. Next, based on environmental damage costs and abatement costs in different pollution mitigation scenarios, the best (most environmentally cost-effective) scenarios were evaluated. The eco-efficiencies of the energy policies were assessed based on two proxies of environmental impacts: the reduced damage cost as a function of the reduced emission rates of the pollutants and the decreased number of polluted days in the year. In a sample area in which the simulated concentrations were verified by local measurements, the most efficient mitigation scenario would decrease the average concentration of PM2.5 by 35% in 2030 and the number of polluted days by 20%. These findings indicate how far the linear functionality of the damage cost from emission levels may mislead environmental impact assessments. This is due to neglecting the source distribution effects and geographical conditions of the environment.

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

We developed a hybrid chemical transport model and receptor model (CTM-RM) to conduct source apportionment of both primary and secondary PM2.5 (particulate matter ≤2.5 μm in diameter) at 36 km resolution throughout the U.S. State of Georgia for the years 2005 and 2007. This novel source apportionment model enabled us to estimate and compare associations of short-term changes in 12 PM2.5 source concentrations (agriculture, biogenic, coal, dust, fuel oil, metals, natural gas, non-road mobile diesel, non-road mobile gasoline, on-road mobile diesel, on-road mobile gasoline, and all other sources) with emergency department (ED) visits for pediatric respiratory diseases.ED visits for asthma (N = 49,651), pneumonia (N = 25,558), and acute upper respiratory infections (acute URI, N = 235,343) among patients aged ≤18 years were obtained from patient claims records. Using a case-crossover study, we estimated odds ratios per interquartile range (IQR) increase for 3-day moving average PM2.5 source concentrations using conditional logistic regression, matching on day-of-week, month, and year, and adjusting for average temperature, humidity, and holidays. We fit both single-source and multi-source models.We observed positive associations between several PM2.5 sources and ED visits for asthma, pneumonia, and acute URI. For example, for asthma, per IQR increase in the source contribution in the single-source model, odds ratios were 1.022 (95% CI: 1.013, 1.031) for dust; 1.050 (95% CI: 1.036, 1.063) for metals, and 1.091 (95% CI: 1.064, 1.119) for natural gas. These sources comprised 5.7%, 2.2%, and 6.3% of total PM2.5 mass, respectively. PM2.5 from metals and natural gas were positively associated with all three respiratory outcomes. In addition, non-road mobile diesel was positively associated with pneumonia and acute URI.

Source-specific impacts of PM2.5 on mortality in the Greater Metropolitan Region of Sydney, Australia

PDS 76: Source specific outdoor air pollution studies, Exhibition Hall (PDS), Ground floor, August 26, 2019, 4:30 PM - 5:30 PM Background: Quantifying the health impacts of air pollution sources is important for policy making as interventions to reduce air pollution are generally source-specific. We assessed the impact on mortality of major sources of particulate air pollution less than 2.5 micrometres in diameter (PM2.5) in Sydney, Australia. Methods: We used a Chemical Transport Model (CTM) to estimate source specific exposure to primary and secondary PM2.5 for the year July 2010 to June 2011. Health Impact Assessment methods were used to estimate the burden of attributable mortality from PM2.5 as well as the number of life-years that would be gained if emissions from major sources were reduced. Results: Wood heaters were the largest source of PM2.5 exposure, responsible for around 24% of total anthropogenic PM2.5 while on-road sources were responsible for 17% of anthropogenic PM2.5. Around 1.2% (5,900 YLL) of all mortality was attributable to long term exposure to all sources of anthropogenic PM2.5 and of this, 0.3% (1,400 YLL) was attributable to wood heater–related PM2.5, with 0.2% (990 YLL) attributable to on-road sources and 0.1% (620 YLL) attributable to power stations. Compared to ongoing emissions at 2010/11 levels a sustained reduction in emissions from wood heaters that complied with a certified emission standard of 1.5 grams of particles per kilogram of wood burned would produce an estimated 90,000 life-years among the cohort of people alive in 2010/11. Conclusions: Eight sources are responsible for more than 60% of exposure to anthropogenic PM2.5 in Sydney, Australia. While the burden of mortality attributable to each PM2.5 source is relatively small, interventions that achieve sustained reductions in emissions from these sources, particularly wood heater, could provide substantial health benefits in Sydney that are likely to outweigh costs.

PM2.5-Associated Health Impacts of Beehive Coke Oven Ban in China.

Historically, beehive coke ovens (BCOs) were extensively operated in China and emitted large quantities of pollutants, including primary PM2.5 and secondary PM2.5 precursors, and other climate forcers. Although these ovens were legally banned in 1996 by the Coal Law, the process of phasing them out took over a decade to accomplish. Based on historical operation data derived from remote sensing images, temporal trends and the spatial distribution of the emissions of various pollutants from BCOs were compiled and used to model the resulting perturbation in ambient PM2.5, population exposure, and PM2.5-associated adverse health impacts. Historically, PM2.5 originating from BCOs affected a vast region across China, which peaked in approximately 1996 and decreased afterward until the ovens' final elimination in 2011. According to the results of a supply-demand model, emissions from the BCOs would have continued to increase after 1996 if they had not been banned. As a result, national average PM2.5 attributable to BCOs in 2014 would have been more than three times as high as that in 1996. It was estimated that the cumulative number of premature deaths associated with BCO-originating PM2.5 from 1982 to 2014 was as high as 365 000 (95% confidence interval 259 000-402 000). The number would have nearly tripled if BCOs had not been banned and halved if the ban had been implemented immediately after the regulation was in force, suggesting the importance of legislation implementation.

Estimation of the Local Human Health Burden Due to Inhalation of Fine Particulate Matter and Identification of Major Emission Sources Using A Life Cycle Impact Assessment Method

Objectives This study determined the local human health burden of fine particulate matter (PM2.5) as disability adjusted life years (DALY) for three Korean cities: Jeonju, Iksan and Changwon. Major air pollutants and emission sources contributing the local human health burden were identified by using a life cycle impact assessment (LCIA) method. Methods The human health burden of PM2.5 was determined by using two methods. First, the PM2.5 concentration-health response method used the concentration-response factor and severity factor for specific age groups over 30 years old and the annual average PM2.5 concentrations to determine DALY for cardiopulmonary diseases and lung cancer. Second, the LCIA used the intake fraction, the effect factor and the local air pollutant emission to quantify DALY due to inhalation of primary and secondary PM2.5. Results and Discussion The human health burden of PM2.5 (DALY/1000 capita) of Jeonju, Iksan and Changwon was respectively determined as 25.44, 35.03 and 22.24 by the PM2.5 concentration-health response method. The LCIA resulted in the human health burden (DALY/1000 capita) of Jeonju, Iksan and Changwon as 4.22, 2.50 and 3.99, respectively. In contribution analysis for DALY, the primary PM2.5 accounted for 60% of the DALY of Jeonju and 71% of the DALY of Changwon. However, NH3 was the major contributor to the Iksan DALY, 49%. Further contribution analysis on the local major emission sources and pollutants showed as follows: The Jeonju DALY was contributed by PM2.5 of “Dust” 21.1%, NH3 of “Agriculture” 13.6% and PM2.5 of “Road vehicles” 13.5%; The Iksan DALY was contributed by NH3 of “Agriculture”, 47.6%, PM2.5 of “Dust” 16.1% and PM2.5 of “Biomaterial incineration” 8.4%; The Changwon DALY was contributed by PM2.5 of “Industrial combustion” 32.6%, PM2.5 of “Non-road engines” 12.7% and PM2.5 of “Dust” 10.0%. Conclusions The LCIA method can be utilized to identify the major emission sources and pollutants contributing to the local burden of disease and help the authority screening priorities. The distinct differences in the DALY of the same city between two methods may imply that the local burden of disease would be dominantly determined by flowing primary and secondary PM2.5 from the outside of the city boundary. Key words: Fine particulate matter, PM2.5, Disability adjusted life years, Human health burdens, Life cycle impact assessment, Air pollutant emission

Trends of outdoor air pollution and the impact on premature mortality in the Pearl River Delta region of southern China during 2006–2015

Severe air pollution in the Pearl River Delta (PRD) region of southern China has increased attention of both the scientific community and policy makers. Air quality data collected at the PRD Regional Air Quality Monitoring Network during 2006–2015 were analyzed for assessing the effectiveness of pollution control measures and for estimating the trends of premature mortality attributable to ambient PM2.5 and O3. Statistically significant decreasing trends were detected for PM2.5 (−1.74 to −1.83 μg m−3 yr−1), PM10 (−2.70 to −2.78 μg m−3 yr−1), NO (−0.61 to −0.74 μg m−3 yr−1), NO2 (−1.20 to −1.22 μg m−3 yr−1), and SO2 (−3.46 to −4.01 μg m−3 yr−1), while an increasing trend was found for O3 (0.70–0.86 μg m−3 yr−1) during the study period. The findings demonstrate the effectiveness of control measures implemented in the last decade for primary pollutants and also indicate the challenges for controlling secondary pollutants. The PM2.5-related premature deaths varied little, e.g., from 40.6 thousand deaths in 2006 to 40.4 thousand deaths in 2015, due to the two contrasting factors, i.e., the decreased PM2.5 concentration and increased population. The increases in both O3 concentration and exposed population resulted in a significant rising trend for the O3-related premature deaths, which increased from 2.7 thousand deaths in 2006 to 4.5 thousand deaths in 2015, at a rate of 165 deaths yr−1. Consistent with the spatial distribution of air pollution and population density, high levels of premature deaths from PM2.5 and O3 were located in the central PRD including Guangzhou, Foshan, Dongguan, and Shenzhen. Decreasing PM2.5 concentration is the most effective way in reducing the regional mortality burden from air pollution in the near future. Besides controlling primary emissions of PM2.5, reducing VOCs emissions is also important for limiting atmospheric oxidizing capacity and associated secondary PM2.5 formation.

Investigating fine particulate matter sources in Salt Lake City during persistent cold air pool events

During wintertime, Salt Lake City experiences periods of high-pressure atmospheric stagnation that leads to prolonged periods with stable atmospheric boundary layers (ABL) due to the unique orography of the region. These atmospheric conditions typically lead to persistent cold air pool (PCAP) events, where the stable ABL remains over several days, degrading air quality in the Salt Lake Valley. In this work, we applied novel source apportionment methods to identify sources that were the greatest contributors to elevated secondary PM2.5 concentrations during PCAP events in winter 2007. A source-oriented modeling approach that relies on emissions inventory data (i.e., source-specific data) was utilized for source characterization. A correction was applied to chemical transport model (CTM) outputs to improve the estimates of source contributions to secondary PM2.5 components (organic carbon, nitrate, sulfate, and ammonium). As a result, gasoline vehicles (25%), natural gas combustion (21%), and other sources (29%) were identified as the largest sources of nitrate, which was the dominant aerosol component during the PCAP periods ([NO3−]max = 37.5 μg m−3). Further, receptor-oriented source apportionment modeling was conducted using the chemical mass balance method with gas constraints (CMB-GC) with location-specific, mathematically optimized PM2.5 source profiles to examine the multi-year trend in PCAP pollution. These optimized profiles were enhanced by integrating the initial guess profiles with CTM results and observations. CMB-GC was applied for the period 2007–2015 at an urban monitor, and the use of seasonally varying optimized source profiles improved results by resolving more mass than when using static source profiles, increasing the predicted-observed PM2.5 mass ratio from 0.91 to 1.03. The averaged biomass burning impacts exhibited the most significant change, increasing from an average of 0.85 to 1.87 μg m−3 when using the static profiles and the optimized profiles, respectively. The static and optimized CMB-GC results were similar for secondary components but differed slightly for mobile impacts. Stratification of PCAP vs. non-PCAP CMB-GC source impacts revealed that the dominant sources of PM2.5 varied under the two meteorological conditions.

Impacts of household sources on air pollution at village and regional scales in India

Abstract. Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM2.5 and O3) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM2.5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM2.5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µg m−3, and, on occasion, exceeded 180 µg m−3. PM2.5 levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM2.5 levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM2.5 produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM2.5 during the periods studied at the four locations averaged about 30 µg m−3, representing approximately 30 % and 20 % of total PM2.5 levels in the rural and urban stations, respectively.

Spatial and Temporal Assessment of PM10 Levels within the City of Tshwane, South Africa

The City of Tshwane (CoT) Municipality is located between three declared air quality priority areas (i.e. Highveld, Vaal Triangle Air-Shed and Waterberg Bojanala priority areas). This city is growing rapidly, and its air quality is suspected to be declining. The main purpose of this study is to identify areas that have high PM10 (particulate matter of aerodynamic diameter 10mm) concentration levels which serve as indicator of air pollution hotspots within the study area. The secondary PM10 data that was collected using the tapered element oscillating microbalance (TEOM) devices in the period from 2009 - 2014 at seven (7) ground based ambient air quality monitoring stations within the city was analysed.

Triggering of cardiovascular hospital admissions by source specific fine particle concentrations in urban centers of New York State

BackgroundPrevious work reported increased rates of acute cardiovascular hospitalizations associated with increased PM2.5 concentrations in the previous few days across urban centers in New York State from 2005 to 2016. These relative rates were higher after air quality policies and economic changes resulted in decreased PM2.5 concentrations and changes in PM composition (e.g. increased secondary organic carbon), compared to before and during these changes. Changes in PM composition and sources may explain this difference. ObjectivesTo estimate the rate of acute cardiovascular hospitalizations associated with increases in source specific PM2.5 concentrations. MethodsUsing source apportioned PM2.5 concentrations at the same NYS urban sites, a time-stratified case-crossover design, and conditional logistic regression models adjusting for ambient temperature and relative humidity, we estimated the rate of these acute cardiovascular hospitalizations associated with increases in mean source specific PM2.5 concentrations in the previous 1, 4, and 7 days. ResultsInterquartile range (IQR) increases in spark-ignition emissions (GAS) concentrations were associated with increased excess rates of cardiac arrhythmia hospitalizations (2.3%; 95% CI = 0.4%, 4.2%; IQR = 2.56 μg/m3) and ischemic stroke hospitalizations (3.7%; 95% CI = 1.1%, 6.4%; 2. 73 μg/m3) over the next day. IQR increases in diesel (DIE) concentrations were associated with increased rates of congestive heart failure hospitalizations (0.7%; 95% CI = 0.2% 1.3%; 0.51 μg/m3) and ischemic heart disease hospitalizations (0.8%; 95% CI = 0.3%, 1.3%; 0.60 μg/m3) over the next day, as hypothesized. However, secondary sulfate PM2.5 (SS) was not. Increased acute cardiovascular hospitalization rates were also associated with IQR increases in concentrations of road dust (RD), residual oil (RO), and secondary nitrate (SN) over the previous 1, 4, and 7 days, but not other sources. ConclusionsThese findings suggest a role of several sources of PM2.5 in New York State (i.e. traffic emissions, non-traffic emissions such as brake and tire wear, residual oil, and nitrate that may also reflect traffic emissions) in the triggering of acute cardiovascular events.

Isotopic evaluation on relative contributions of major NOx sources to nitrate of PM2.5 in Beijing

Nitrate (NO3−) is a key component of secondary inorganic aerosols and PM2.5. However, the contributions of nitrogen oxides (NOx) emission sources to NO3− in PM2.5 remain poorly constrained. This study measured nitrogen (N) isotopes of NO3− (hereafter as δ15N-NO3-) in PM2.5 collected at Beijing in 2014. We observed that δ15N-NO3- values in PM2.5 (−2.3‰ − 19.7‰; 7.3 ± 5.4‰ annually) were significantly higher in winter (11.9 ± 4.4‰) than in summer (2.2 ± 2.5‰). The δ15N differences between source NOx and NO3− in PM2.5 (hereafter as Δ values) were estimated by a computation module as 7.8 ± 2.2‰ − 10.4 ± 1.6‰ (8.8 ± 2.4‰). Using the Δ values and δ15N values of NOx from major fossil (coal combustion, vehicle exhausts) and non-fossil sources (biomass burning, microbial N cycle), contributions of major NOx sources to NO3− in PM2.5 were further estimated by the SIAR model. We found that seasonal variations of δ15N-NO3- values in PM2.5 of Beijing were mainly caused by those of NOx contributions from coal combustion (38 ± 10% in winter, 20 ± 9% in summer). Annually, NOx from coal combustion, vehicle exhausts, biomass burning, and microbial N cycle contributed 28 ± 12%, 29 ± 17%, 27 ± 15%, and 16 ± 7% to NO3− in PM2.5, respectively, showing actually comparable contributions between non-fossil NOx (43 ± 16%) and fossil NOx (57 ± 21%). These results are useful for planning the reduction of NOx emissions in city environments and for elucidating relationships between regional NOx emissions and atmospheric NO3− pollution or deposition.

Effects of Emission Reductions of Key Sources on the PM2.5 Concentrations in the Yangtze River Delta

The effects of emission reductions of key sources (eight key industries and transportation) on the PM2.5 concentrations in the Yangtze River Delta (YRD) were investigated using the weather research forecast-chemistry (WRF-Chem) model in 2013 combined with two normal and enhanced emission reduction scenarios. The SO2, NOx, PM2.5, and NMVOC emissions in the YRD decrease by 36.3%, 26.3%, 32.0%, and 14.6% and by 51.4%, 39.6%, 37.6%, and 28.4% under the normal and enhanced emission reduction scenarios, respectively. The simulation results show that the annual mean PM2.5 concentrations over the national environmental monitoring sites in the YRD decline by 1.4-26.7 μg·m-3 and 2.1-32.3 μg·m-3, reflecting a decrease of 2.7%-23.1% and 3.9%-27.5%, under the two emission reduction scenarios, respectively. The nitrate in secondary inorganic aerosols contributes the most to the reduction of the annual mean PM2.5 concentration. The seasonal variation characteristics of the PM2.5 and secondary inorganic aerosol concentrations reflect that the smallest and largest declining rates occur in winter and summer, respectively. With increasing emission reduction, the declining rates of PM2.5 and the secondary inorganic aerosol concentrations in summer increase more compared with those in other seasons, resulting in a greater seasonal variation of the rates. The PM2.5 concentrations decrease by~20% in Shanghai and the Jiangsu Province under the enhanced emission scenario in summer. The analysis of the atmospheric oxidation shows that the atmospheric oxidation capacity is enhanced to different degrees by emission reductions of key sources in all seasons; it is further enhanced with increasing emission reduction. The enhanced oxidation capacity favors the formation of secondary PM2.5, thereby hindering the reduction of the PM2.5 concentration. The strongest hindrance occurs in winter, resulting in the worst PM2.5 pollution improvement. The atmospheric oxidation capacity is less affected by emission reductions of key sources in summer, making PM2.5 pollution improvement most effective. Furthermore, the negative effects of the enhancement of the atmospheric oxidation capacity on the reduction of the PM2.5 concentration in spring and autumn cannot be ignored.

Dispatch for Urban Integrated Heat and Power System Considering Secondary PM2.5 Under Smart Environmental Sensing

To improve the air quality and reduce the human settlement pollution, the effects of initial air pollutant emissions from urban heat and power generation with the diffusion of pollutants were often considered in the energy system dispatch, but the effects of secondary PM2.5 hazards caused by the urban energy system were almost ignored. In the urban integrated heat and power systems (UIHPS), under the support of smart environmental sensing, the introduction of integrated demand response (IDR) between the energy production side and consumer side is an effective means of reducing primary and secondary PM2.5 hazards. As for the diffusion of primary pollutants and the generation of secondary PM2.5 are affected by the uncertainty of weather conditions, a stochastic environmental economic dispatch (SEED) model for UIHPS considering IDR is proposed in this paper. Linearized secondary PM2.5 generation and diffusion functions for UIHPS dispatch are introduced into the objective function. The price-based IDR is performed on the Stackelberg game mechanism, and the game equilibrium solution is transformed as the SEED's constraint set by using the backward induction method. Simulations show that under the partly data supply from smart environmental sensing, the SEED model considering the secondary PM2.5 and IDR can effectively alleviate the comprehensive hazard of PM2.5 under severe haze weather.

Responses of Secondary Inorganic PM2.5 to Precursor Gases in an Ammonia Abundant Area In North Carolina

ABSTRACT Secondary inorganic fine particulate matter (iPM2.5) constitutes a significant amount of the atmospheric PM2.5. The formation of secondary iPM2.5 is characterized by thermodynamic equilibrium gas-particle partitioning of gaseous ammonia (NH3) and aerosol ammonium (NH4+). To develop effective strategies for controlling atmospheric PM2.5, it is essential to understand the responses of secondary iPM2.5 to different precursor gases. In southeastern North Carolina, the amount of NH3 is in excess to fully neutralize acidic gases (i.e., NH3-rich conditions). NH3-rich conditions are mainly attributed to the significant NH3 emissions in the region, especially from the large amounts of animal feeding operation (AFO). To gain a better understanding of the impact of NH3 on the formation of secondary iPM2.5 in this area, the responses of iPM2.5 to precursor gases under different ambient conditions were investigated based upon three-year monitoring data of the chemical components in iPM2.5, gaseous pollutants, and meteorological conditions. The gas ratio (GR) was used to assess the degree of neutralization via NH3, and ISORROPIA II model simulation was used to examine the responses of iPM2.5 to changes in the total NH3, the total sulfuric acid (H2SO4), and the total nitric acid (HNO3). It was discovered that under different ambient temperature and humidity conditions, the responses of iPM2.5 to precursor gases vary. In general, iPM2.5 responds nonlinearly to the total NH3 but linearly to the total H2SO4 and the total HNO3. In NH3-rich regions, iPM2.5 is not sensitive to changes in the total NH3, but it is very sensitive to changes in the total H2SO4 and/or the total HNO3. Reducing the total H2SO4, as opposed to the total HNO3 or the total NH3, leads to a significant reduction in iPM2.5 and is thus a more effective strategy for decreasing the concentration of iPM2.5. This research provides insight into controlling and regulating PM2.5 in NH3-rich regions.

Spatial and Temporal Variations of PM2.5 in North Carolina

Studies have indicated that the adverse effects on human health and the decrease in visibility caused by fine particulate matter (PM2.5) exhibit spatial heterogeneity. Moreover, the environmental effects produced by different chemical compositions of PM2.5 vary on a regional scale. Therefore, understanding the spatiotemporal variations and chemical compositions of PM2.5 is necessary for assessing the regional impacts. Secondary inorganic PM2.5 (iPM2.5) is formed through chemical reactions between the base gas NH3 and acidic gas pollutants (e.g., NO2 or SO2). The major components of iPM2.5 include NH4+, SO42–, and NO3–. To fully comprehend the regional impacts of PM2.5, this research quantifies the spatiotemporal variations of iPM2.5 with the aim of evaluating the contributions from iPM2.5 to PM2.5 in North Carolina (NC). The concentrations (at 34 sites) and chemical components (at 7 sites) of PM2.5 from 2005 to 2014 were extracted from the EPA’s AirData, with the highest concentrations measured in the urban areas of central NC. Notably, PM2.5 concentrations have been significantly reduced over the past 10 years, with a concurrent decreasing trend in iPM2.5. Seasonal variation analysis indicates that PM2.5 concentrations were higher in summer and lower in winter; however, significant variation occurred only between 2005 and 2011. Although iPM2.5 formed the largest mass fraction of PM2.5 for 2005–2011, organic carbon matter (OCM) contributed the dominant share for 2012–2014. Significant seasonal variations in the iPM2.5 mass fractions were also observed, with NO3– and SO42– exhibiting inverse variations. This study links the ambient PM2.5 to various sources by revealing the spatiotemporal variations of PM2.5 and their associated chemical compositions in NC, thereby enabling the development of effective control and mitigation strategies.

Heavy Particulate Matter Pollution during the 2014-2015 Winter in Tianjin, China

Tianjin, a city located in North China, is heavily polluted with frequent haze, particularly in winter. In this study, continuous online field observations of the sulfate, nitrate, ammonium, PM2.5, and gaseous pollutant concentrations in addition to the meteorological parameters were conducted in Tianjin during the 2014–2015 winter (December–January–February). The PM2.5 concentrations ranged from 5.6 to 495.6 µg m–3, with an average value of 112.2 ± 96.1 µg m–3. The worst pollution was observed in January, when levels on 10% of the days exceeded 250 µg m–3, qualifying as severely polluted, and four haze episodes, each lasting 5–7 days, occurred in rapid succession (separated by ~1-day intervals). Back-trajectory and chemical composition analysis suggested that elevated levels of secondary ionic aerosol species were a primary cause of these episodes. Regional transport of PM2.5 also played a large role in the formation of haze. CO was selected as an inactive chemical tracer in studying the chemical process of PM2.5 formation. The results indicated that when high concentrations of PM2.5 were present, the formation of secondary PM2.5 increased while the photochemical production of O3 ceased. The sulfur oxidation ratio [SOR = nSO42–/(nSO42– + nSO2); n refers to the molar concentration] and nitrate oxidation ratio [NOR = nNO3–/(nNO3– + nNO2)] increased with the PM2.5 level, and heterogeneous processes on the surfaces of fine particles rather than photochemistry drove the haze events. This research elucidates haze formation mechanisms, which must be understood in order to create effective control policies in Tianjin.

Air pollution exposure and lung function until age 16 years: the PIAMA birth cohort study.

Evidence for the effects of air pollution exposure on lung function growth into adolescence is scarce. We investigated associations of air pollution exposure with lung function and lung function growth until age 16.We conducted both longitudinal (n=915) and cross-sectional (n=721) analyses of associations of air pollution exposure with forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) growth from ages eight to 16 and FEV1 and FVC at age 16. We estimated residential concentrations of nitrogen dioxide (NO2), "soot" and particulate matter (PMx, where x is the 50% cut-off aerodynamic diameter in µm) with diameters of <2.5 µm (PM2.5), <10 µm (PM10) and 2.5-10 µm (PMcoarse) during the preschool, primary school and secondary school time windows by land use regression models. Associations with (growth in) FEV1 and FVC were analysed by linear (mixed effects) regression.Higher air pollution exposure was associated with reduced FEV1 growth (e.g. adjusted difference -0.26% (95% CI -0.49 to -0.03%) per interquartile range increase in secondary school PM2.5) and lower FEV1 (adjusted difference -2.36% (95% CI -3.76 to -0.94%)), but was not adversely associated with FVC. Associations with FEV1 were stronger in boys than girls and were not modified by asthma status.Higher air pollution exposure may lead to increased airway obstruction, but not reduced lung volume in adolescence.

Forecasting PM10 hourly concentrations in northern Italy: Insights on models performance and PM10 drivers through self-organizing maps

A linear and an artificial neural network (ANN) statistical model have been developed and validated for short-term forecasting of PM 10 hourly concentrations in the city of Brescia (Italy). PM 10 observed concentrations were biased by less than 1% by each model, though the ANN outperformed the linear model, as exhibiting NRMSE of 0.48 vs. 0.53, and r 2 of 0.57 vs. 0.48. The self-organizing maps (SOMs) showed that both models predictions exhibit the same clustering as the observations, with the ANN at worst capable of under-estimating clustered PM 10 peak concentrations by 5.8 μg/m 3 . In Brescia, PM 10 most critical conditions were detected in wintertime in the early morning or late afternoon under unfavourable meteorological conditions, i.e. reduced advection enhancing PM 10 stagnation, and lack of precipitations capable of reducing PM 10 resuspension. Under these conditions, PM 10 accumulation is driven by local anthropogenic emissions ascribing to two main sources: heating plants, responsible of emissions of primary PM 10 (mostly PM 2.5 , likely resulting from wood and biomass burning); and road traffic (basically diesel vehicles), mainly responsible of emissions of secondary PM 10 precursors (mostly NO x ), and secondly of primary PM 10 emissions. The SOM analysis clearly indicated that PM 10 most critical conditions are driven by the secondary rather primary PM 10 component. • A linear and an ANN model developed and validated to forecast PM 10 1-h concentrations in Brescia (Italy). • The ANN outperforms the linear model, with NRMSE of 0.48 vs. 0.53, and r 2 of 0.57 vs. 0.48 • SOMs proved to recognise the cluster structure of all drivers controlling PM 10 concentrations. • Heating plants and road traffic were confirmed as main anthropogenic emission sources. • The SOM analysis indicated that PM 10 most critical conditions are driven by the secondary rather primary PM 10 component.