Primary PM Emissions Research Articles

Major source categories of PM2.5 oxidative potential in wintertime Beijing and surroundings based on online dithiothreitol-based field measurements

Fine particulate matter (PM2.5) causes millions of premature deaths each year worldwide. Oxidative potential (OP) has been proposed as a better metric for aerosol health effects than PM2.5 mass concentration alone. In this study, we report for the first time online measurements of PM2.5 OP in wintertime Beijing and surroundings based on a dithiothreitol (DTT) assay. These measurements were combined with co-located PM chemical composition measurements to identify the main source categories of aerosol OP. In addition, we highlight the influence of two distinct pollution events on aerosol OP (spring festival celebrations including fireworks and a severe regional dust storm). Source apportionment coupled with multilinear regression revealed that primary PM and oxygenated organic aerosol (OOA) were both important sources of OP, accounting for 41 ± 12 % and 39 ± 10 % of the OPvDTT (OP normalized by the sampled air volume), respectively. The small remainder was attributed to fireworks and dust, mainly resulting from the two distinct pollution events. During the 3.5-day spring festival period, OPvDTT spiked to 4.9 nmol min−1 m−3 with slightly more contribution from OOA (42 ± 11 %) and less from primary PM (31 ± 15 %). During the dust storm, hourly-averaged PM2.5 peaked at a very high value of 548 μg m−3 due to the dominant presence of dust-laden particles (88 % of total PM2.5). In contrast, only mildly elevated OPvDTT values (up to 1.5 nmol min−1 m−3) were observed during this dust event. This observation indicates that variations in OPvDTT cannot be fully explained using PM2.5 alone; one must also consider the chemical composition of PM2.5 when studying aerosol health effects. Our study highlights the need for continued pollution control strategies to reduce primary PM emissions, and more in-depth investigations into the source origins of OOA, to minimize the health risks associated with PM exposure in Beijing.

Pathways toward PM2.5 air quality attainment and its CO2 mitigation co-benefits in China's northern cities by 2030

Rapid economic development and urbanization have left millions of people in urban areas vulnerable to the adverse effects of air pollution. Reducing serious PM2.5 pollution remains a major policy challenge for Chinese cities, such as the “2 + 26” Cities in the most polluted region of China, despite adopting various air pollution-control measures since 2017. In this study, we use a Greenhouse Gas and Air Pollution Interactions and Synergies model for the “2 + 26” Cities to explore feasible pathways for achieving the PM2.5 air quality annual standard (35 μg/m3) until 2030 by developing four future scenarios based on a 2017 emission inventory. The results show that by continuing the control policies from before 2020, including the Air Pollution Prevention and Control Action Plan from 2013 and the Three-Year Action Plan for Wining the Blue Sky Defense Battle from 2018, and implementing the policies likely to be proposed after 2020, the “2 + 26” Cities will not succeed in achieving the standard by 2030; furthermore, the amount of the population living below 35 μg/m3 will not exceed 12%. Nevertheless, by combining the best available technologies, the regional average PM2.5 concentration in the “2 + 26” Cities is 34 μg/m3 by 2030, attaining the target; moreover, more than half of the population will experience air quality that conforms to the standard. Under this scenario, emissions of primary PM2.5, SO2, NOx, NH3 and NMVOCs are estimated to decrease by 75%, 69%, 76%, 32% and 52%, respectively, in 2017–2030. The emission reductions under alternative scenarios vary widely across cities, indicating the need for differentiated control strategies among cities. Considering the predominant contributions to emissions and the lack of effective control measures, fuel conversion, off-road, agricultural and solvent use sectors should be paid more attention to enable further emission reductions in future. In addition, CO2 emission reduction co-benefits of implemented clean air action measures have been identified; however, the negative reduction effects of these measures should be given more attention (e.g., increased carbon emissions in the power sector from electrification of end-users).

Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling

Hourly PM2.5 speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM2.5 components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM2.5 water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMFwhole). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMFwhole solution. After removing event data, PMF modeling was conducted for individual months (PMFmonth) and the 4-year period (PMF4-year), respectively. PMFmonth solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF4-year solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

Changes of PM2.5 population exposure and its sources in the US from 1990 to 2010

KEYWORDS PM25, air pollution sources, exposure BACKGROUND AND AIM The quantification of the contributions of sources of air pollutants, especially PM2.5, can be used for the improvement of our understanding of PM2.5 health effects. Three-dimensional chemical transport models are well suited to address this problem, since they simulate all the major processes that impact PM2.5 concentrations and transport. In this work, we quantified the changes in the concentration, exposure, composition, and sources of PM2.5 in the US. Significant reductions of emissions of SO2, NOx, VOCs and primary PM have taken place. METHODS We evaluate our understanding of the links between these emissions concentration and exposure changes combining a chemical transport model (PMCAMx) with the Particle Source Apportionment Algorithm. RESULTS Results for 1990, 2001 and 2010 are presented. The 63% reduction in PM2.5 sulfate concentrations from electrical generation units during these 20 years has led to a 60% reduction in PM2.5 sulfate exposure. Also, the reductions in elemental carbon (EC) concentrations from road transport by 72% have led to a reduction of PM2.5 EC exposure of 70%. CONCLUSIONS In 1990 90% of the US population was exposed to PM2.5 concentrations to equal and higher than the suggested annual mean by the WHO, but this reduced to 70% in 2010. These results are the basis for an epidemiological study linking PM2.5 sources in the US and their health effect (Pond et al., 2021). REFERENCES Pond, Z. A., Hernandez, C. S., Adams, P. J., Pandis, S. N., Garcia, G. R., Robinson, L. A., Marshall, J. D., Burnett, R., Skyllakou, K., Rivera, P. G., Karnezi, E., Coleman, C. J., and Pope, A. C.: Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort, Env. Science & Tech. Article ASAP, 2021.

Secondary Organic and Inorganic Aerosol Formation from a GDI Vehicle under Different Driving Conditions

This study investigated the primary emissions and secondary aerosol formation from a gasoline direct injection (GDI) passenger car when operated over different legislative and real-world driving cycles on a chassis dynamometer. Diluted vehicle exhaust was photooxidized in a 30 m3 environmental chamber. Results showed elevated gaseous and particulate emissions for the cold-start cycles and higher secondary organic aerosol (SOA) formation, suggesting that cold-start condition will generate higher concentrations of SOA precursors. Total secondary aerosol mass exceeded primary PM emissions and was dominated by inorganic aerosol (ammonium and nitrate) for all driving cycles. Further chamber experiments in high temperature conditions verified that more ammonium nitrate nucleates to form new particles, forming a secondary peak in particle size distribution instead of condensing to black carbon particles. The results of this study revealed that the absorption of radiation by black carbon particles can lead to changes in secondary ammonium nitrate formation. Our work indicates the potential formation of new ammonium nitrate particles during low temperature conditions favored by the tailpipe ammonia and nitrogen oxide emissions from gasoline vehicles.

Spatial Distribution of Primary and Secondary PM2.5 Concentrations Emitted by Vehicles in the Guanzhong Plain, China

With the rapid increase of the vehicle population in the Guanzhong Plain (GZP), the fine particulate matter (PM2.5) emitted by vehicles has an impact on regional air quality and public health. The spatial distribution of primary and secondary PM2.5 concentrations from vehicles in GZP in January and July 2017 was simulated in this study by using the Weather Research and Forecasting (WRF) model and the California Puff (CALPUFF) air quality model. The contributions of vehicle-related emission sources to total PM2.5 concentrations were also calculated. The results show that although the emissions of primary PM2.5, NOx, and SO2 in July were greater than those in January, the hourly average concentrations of primary and secondary PM2.5 in January were significantly higher than those in July. The highest concentrations of primary and total PM2.5 were mostly located in the urban areas of Xi’an and Xianyang in the central region of GZP. The contributions of exhaust emissions, secondary nitrates, brake wear, tire wear, and secondary sulfate to the total PM2.5 concentrations in GZP were 50.37%, 34.76%, 10.79%, 4.06%, and 0.04% in January and 71.91%, 11.14%, 11.89%, 5.03%, and 0.03% in July, respectively. These results will help us to further control PM2.5 pollution caused by vehicles.

Coal Is Dirty, but Where It Is Burned Especially Matters.

Coal abatement actions for pollution reduction often target total coal consumption. The health impacts of coal uses, however, vary extensively among sectors. Here, we modeled the sectorial contributions of coal uses to emissions, outdoor and indoor PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 mm) concentrations, exposures, and health outcomes in China from 1970 to 2014. We show that in 2014, residential coal accounted for 2.9% of total energy use but 34% of premature deaths associated with PM2.5 exposure, showing that effects were magnified substantially along the causal path. The number of premature deaths attributed to unit coal consumption in the residential sector was 40 times higher than that in the power and industrial sectors. Emissions of primary PM2.5 were more important than secondary aerosol precursors in terms of health consequences, and indoor exposure accounted for 97% and 91% of total premature deaths attributable to PM2.5 from coal combustion in 1974 and 2014, respectively. Our assessment raises a critical challenge in the switching of residential coal uses to effectively mitigate PM2.5 exposure in the Chinese population.

Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy

Covid19-induced lockdown measures caused modifications in atmospheric pollutant and greenhouse gas emissions. Urban road traffic was the most impacted, with 48–60% average reduction in Italy. This offered an unprecedented opportunity to assess how a prolonged (∼2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020–30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019–02/05/2019). NO2, O3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data.NO2 levels remarkably dropped over all urban areas (from −24.9% in Milan to −59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOCs-limited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of ∼13–17% counterbalanced by increases up to ∼9%. Higher household heating usage (+16–19% in March), also driven by colder weather conditions than 2019 (−0.2 to −0.8 °C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health.

Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning

A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of ~1035). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM2.5) and ozone (O3) over the Pearl River Delta (PRD) region of China. The GA was found to be >99% more efficient than the commonly used grid searching method while providing the same combination of optimized multi-pollutant control strategies. The GA method can therefore address air quality management problems that are intractable using the grid searching method. The annual attainment goals for PM2.5 (< 35 μg m−3) and O3 (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NOx (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM2.5 goals, SO2 reductions (> 9%) are needed as well. The estimated benefit-to-cost ratio of the optimal control strategy reached 17.7 in our application, demonstrating the value of multi-pollutant control for cost-effective air quality management in the PRD region.

Intermediate and high ethanol blends reduce secondary organic aerosol formation from gasoline direct injection vehicles

We evaluated the secondary aerosol formation from two current technology flex fuel vehicles (FFVs) equipped with gasoline direct injection engines when operated on different fuel formulation on a chassis dynamometer. This study explored the influence of ethanol fueling and aromatic concentration on secondary organic aerosol (SOA) production by utilizing two E10 fuels with different aromatic levels and two higher ethanol blends (i.e., E30 and E78). The diluted emissions from both FFVs were photooxidized in a 30 m3 mobile atmospheric chamber with two distinct initial conditions, while the vehicles operated over cold-start and hot-start LA92 test cycles. Testing revealed that one vehicle did not produce secondary aerosol when emissions were photooxidized with a single precursor oxidant. However significant SOA formation occurred in the presence of an anthropogenic volatile organic carbon surrogate and NOx mixture in the chamber, despite the fact that both vehicles had similar primary particulate emission levels. The secondary aerosol formed in an urban airshed was mainly composed of ammonium nitrate and significantly exceeded primary tailpipe PM emissions. Cold-start operation from both vehicles led to greater secondary aerosol production compared to hot-start LA92 operation. SOA production showed a strong relationship with the tailpipe non-methane hydrocarbon (NMHC) emissions and overall reduced with higher ethanol blending in the fuel. The impact of aromatics was clear, with the higher aromatic E10 fuel showing higher SOA formation than the lower aromatic E10 fuel.

Least-cost control strategy optimization for air quality attainment of Beijing–Tianjin–Hebei region in China

Control strategies can be optimized to attain air quality standards at minimal cost through selecting optimal combinations of controls on various pollutants and regional sources. In this study, we developed a module for least-cost control strategy optimization based on a real-time prediction system of the responses of pollution concentrations to emissions changes and marginal cost curves of pollutant controls. Different from other method, in this study the relationship between pollution concentrations to and precursor emissions was derived from multiple air quality simulations in which the nonlinear interactions among different precursor emissions can be well addressed. Hypothetical control pathways were designed to attain certain air quality goals for particulate matter (PM2.5) and ozone (O3) in the Beijing–Tianjin–Hebei region under the 2014 baseline emission level. Results suggest that reducing local primary PM emissions was the most cost-efficient method to attain the ambient PM2.5 standard, whereas for O3 attainment, reducing regional emission sources of gaseous pollutants (i.e., SO2, NOx, and volatile organic compounds (VOCs)) exhibited greater effectiveness. NH3 controls may be cost-efficient in achieving strengthened PM2.5 targets; however, they might not help in reducing O3. To achieve both PM2.5 (<35 μg m−3) and O3 (daily 1-h maxima concentration < 100 ppb) targets in Beijing, the reduced rates in BTH regions of NOx, SO2, NH3, VOCs and primary PM are 75%, 75%, 5%, 55%, and 85%, respectively from the emission levels in the year of 2014. Local reduction is the most effective method of attaining moderate PM2.5 and O3 targets; however, to achieve more aggressive air quality goals, the same level of reductions must be conducted across the whole Beijing–Tianjin–Hebei region.

Nonlinear relationships between air pollutant emissions and PM2.5-related health impacts in the Beijing-Tianjin-Hebei region

A direct and quantitative linkage of air pollution-related health effects to emissions from different sources is critically important for decision-making. While a number of studies have attributed the PM2.5-related health impacts to emission sources, they have seldom examined the complicated nonlinear relationships between them. Here we investigate the nonlinear relationships between PM2.5-related premature mortality in the Beijing-Tianjin-Hebei (BTH) region, one of the most polluted regions in the world, and emissions of different pollutants from multiple sectors and regions, through a combination of chemical transport model (CTM), extended response surface model (ERSM), and concentration-response functions (CRFs). The mortalities due to both long-term and short-term exposures to PM2.5 are most sensitive to the emission reductions of primary PM2.5, followed by NH3, nonmethane volatile organic compounds and intermediate volatility organic compounds (NMVOC+IVOC). The sensitivities of long-term mortality to emissions of primary organic aerosol (POA), NMVOC+IVOC and SO2 do not change much with reduction ratio, whereas the sensitivities to primary inorganic PM2.5 (defined as all chemical components of primary PM2.5 other than POA), NH3 and NOx increase significantly with the increase of reduction ratio. The emissions of primary PM2.5, especially those from the residential and commercial sectors, contribute a larger fraction of mortality in winter (57–70%) than in other seasons (28-42%). When emissions of multiple pollutants or those from both local and regional emissions are controlled simultaneously, the overall sensitivity of long-term mortality is much larger than the arithmetic sum of the sensitivities to emissions of individual pollutants or from individual regions. This implies that a multi-pollutant, multi-sector and regional joint control strategy should be implemented to maximize the marginal health benefits. For NOx emissions, we suggest a nationwide control strategy which significantly enhances the effectiveness for reducing mortality by avoiding possible side effects when only the emissions within the BTH region are reduced.

Ammonia emissions from China-6 compliant gasoline vehicles tested over the WLTC

Ammonia emissions cause several negative impacts on the quality of air, water and soil, while secondary ammonia formed on three-way catalysts (TWCs) of gasoline vehicles has not been sufficiently investigated, especially in the developing countries. In this paper, tailpipe ammonia emissions from seven China-6 certified, low-mileage gasoline vehicles were measured over the World Harmonized Light-duty Test Cycle (WLTC). The results demonstrated that ammonia emissions from the seven test vehicles ranged from 0.65 ± 0.38 mg/km to 8.01 ± 3.12 mg/km. A comparison with the emission factors reported in other research indicates that these China-6 certified vehicles emitted similar or less ammonia emissions compared to the vehicles certified to older EU and federal regulations. The ammonia emissions from the test vehicles showed very weak linkage with their engine or transmission technologies. For all the test vehicles, the vast majority of ammonia emissions were emitted within the engine warm-up period due to already lighted-off catalysts but still enriched air/fuel mixtures. No ammonia emissions were noticed within any engine-start events. The masses of ammonia-converted ammonium salts were estimated assuming that all the tailpipe ammonia emissions will transform into ammonium nitrate and ammonium sulfate, and compared to the primary PM emissions measured with filters. The masses of salts were drastically higher than the primary PM, which underscored the significance of vehicle-related ammonia emissions as a key ambient PM contributor.

Quantifying Impacts of Emission Reductions on Air Pollution Equity and Health Effects in New York City

This study examines inequity in exposure to fine particulate matter (PM2.5) in New York City and surrounding areas across income groups. By contrasting the sensitivities of public health and equity measures to emissions reductions on a location-by-location basis, this study offers novel yet practical suggestions to coordinate air quality management strategies that prioritize different policy endpoints.Air quality simulations were run using the USEPA&amp;#8217;s Community Multiscale Air Quality (CMAQ) model and its adjoint version over New York City at 1 km resolution. CMAQ is used to estimate concentration surfaces for quantifying exposure to PM2.5. Second, the adjoint of CMAQ was run to estimate the sensitivity of domain-wide inequity parameters to pollution emissions, on a location-by-location basis for different sectors.Preliminary results show that lower income populations in New York City tend to be exposed to higher concentrations of PM2.5. We find that emission reductions in Brooklyn, Harlem, and the Bronx would be most beneficial for reducing domain-wide inequity, while reductions in Manhattan would result in increased inequity. We also calculate monetized health benefits per ton of primary PM emissions across the domain, with benefits estimated to be significant (as high as $10M/ton) and spatially variable across the domain. We propose a novel approach for monetizing air pollution inequity, and find that monetized benefits from inequity reductions are comparable in magnitude (up to $5M/ton) to health benefits from avoided mortality. We examine hypothetical scenarios where quantified benefits with regards to health an inequity are considered in tandem to coordinate policies that target both endpoints simultaneously. Our results demonstrate that focusing emission reductions on sources that are influential on both environmental justice and public health can yield improvements across multiple policy goals simultaneously.

Spatial and temporal disaggregation of natural emissions in urban areas in Greece for the period 2010–2013 and future projections

Emission inventories for particulate matter (PM 2.5 and PM 2.5-10 ) from natural sources were created for the Athens and Thessaloniki metropolitan areas and the greater area of Volos for the period 2010–2013. The inventories include primary windblown dust emissions from agricultural and vacant lands, primary sea salt particles emissions from the breaking of waves at the Sea Shore-surf zone and the bursting of bubbles from oceanic whitecaps - Open Ocean, and emissions of BVOCs (Biogenic Volatile Organic Compounds), precursor to PM. The objective of this work was to study the seasonal variation of natural PM emissions in the areas of interest and their spatial allocation. Weighting factors, specific to each pollutant, source and area, were calculated for the disaggregation of annual emissions to monthly and daily values and their allocation on high resolution grids. There was no significant seasonal variation observed in the natural PM emissions while BVOCs emissions were increased during the warm period due to the enhanced solar radiation and temperature. The results of the calculations were compared to values of the period 2000–2010 and it was found that natural BVOCs emissions during the period 2010–2013 were enhanced compared to 2000–2010 whereas natural PM emissions have lowered. Moreover, the analysis focused on the effects from possible future changes in land cover and environmental conditions to the temporal and spatial variation of natural emissions over the areas using specific scenarios. • Monthly emissions of primary PM from natural sources in three urban areas in Greece were calculated. • The BVOCs emissions during the period 2010–2013 were enhanced compared to 2000–2010 whereas natural PM emissions have lowered. • The projected changes in temperature and precipitation did not affect considerably the temporal and spatial distribution of natural emissions. • Considerable emission changes were observed for significant land use modifications together with the prevalence of dry conditions.

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.

Estimation of the Effectiveness of the Current Policies on Coal-related Air Pollution Control

China has been experiencing severe haze pollution mainly due to its fast economic growth and large amount of coal consumption. To deal with the problems, Chinese government has issued a series of policies to control main air pollutants emission. In this paper, the effectiveness of the policies to control the major four coal-consuming sectors, which are coal-fired power plants, coal-related industrial processes, coal-fired boilers and household coal consumption, were evaluated. The main air pollutants emission reduction was estimated and the air quality improvement was simulated using the WRF-CMAQ model. The results showed that by 2017, the emissions of SO2, NOx and primary PM from the four sectors will decrease by 1.37 million tons, 1.28 million tons and 1.55 million tons, respectively, which are 7.4%, 6.9% and 10.1%, compared with 2015. Consequently, the annual average concentration of PM2.5 will decrease by 3ug/m3, a drop of 6%. Implementation of the ultra-low emission standards in the coal-fired power plants is the most effective measures to reduce SO2 and NOx emission, while installation of dust removal facilities in coal related industrial processes contributes most to primary PM emission reduction.

Source apportionment of fine particulate matter in China in 2013 using a source-oriented chemical transport model

China has been suffering high levels of fine particulate matter (PM2.5). Designing effective PM2.5 control strategies requires information about the contributions of different sources. In this study, a source-oriented Community Multiscale Air Quality (CMAQ) model was applied to quantitatively estimate the contributions of different source sectors to PM2.5 in China. Emissions of primary PM2.5 and gas pollutants of SO2, NOx, and NH3, which are precursors of particulate sulfate, nitrate, and ammonium (SNA, major PM2.5 components in China), from eight source categories (power plants, residential sources, industries, transportation, open burning, sea salt, windblown dust and agriculture) were separately tracked to determine their contributions to PM2.5 in 2013. Industrial sector is the largest source of SNA in Beijing, Xi'an and Chongqing, followed by agriculture and power plants. Residential emissions are also important sources of SNA, especially in winter when severe pollution events often occur. Nationally, the contributions of different source sectors to annual total PM2.5 from high to low are industries, residential sources, agriculture, power plants, transportation, windblown dust, open burning and sea salt. Provincially, residential sources and industries are the major anthropogenic sources of primary PM2.5, while industries, agriculture, power plants and transportation are important for SNA in most provinces. For total PM2.5, residential and industrial emissions are the top two sources, with a combined contribution of 40–50% in most provinces. The contributions of power plants and agriculture to total PM2.5 are about 10%, respectively. Secondary organic aerosol accounts for about 10% of annual PM2.5 in most provinces, with higher contributions in southern provinces such as Yunnan (26%), Hainan (25%) and Taiwan (21%). Windblown dust is an important source in western provinces such as Xizang (55% of total PM2.5), Qinghai (74%), Xinjiang (59%). The large variation in sources of PM2.5 across China suggests that PM2.5 mitigation programs should be designed separately for different regions/provinces.

Managing future air quality in megacities: A case study for Delhi

Megacities in Asia rank high in air pollution at the global scale. In many cities, ambient concentrations of fine particulate matter (PM2.5) have been exceeding both the WHO interim targets as well as respective national air quality standards. This paper presents a systems analytical perspective on management options that could efficiently improve air quality at the urban scale, having Delhi as a case study. We employ the newly developed GAINS-City policy analysis framework, consisting of a bottom up emission calculation combined with atmospheric chemistry-transport calculation, to derive innovative insights into the current sources of pollution and their impacts on ambient PM2.5, both from emissions of primary PM as well as precursors of secondary inorganic and organic aerosols. We outline the likely future development of these sources, quantify the related ambient PM2.5 concentrations and health impacts, and explore potential policy interventions that could effectively reduce environmental pollution and resulting health impacts in the coming years. The analysis demonstrates that effective improvement of Delhi's air quality requires collaboration with neighboring States and must involve sources that are less relevant in industrialized countries. At the same time, many of the policy interventions will have multiple co-benefits on development targets in Delhi and its neighboring States. Outcomes of this study, as well as the modelling tools used herein, are applicable to other urban areas and fast growing metropolitan zones in the emerging Asian regions.

Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;, PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt;, and PM&amp;lt;sub&amp;gt;2. 5&amp;lt;/sub&amp;gt; for 2001–2010

Abstract. This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km × 5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model–measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2. 5, and PM10 (daily maximum 8 h running mean for O3). The comparison was temporally and spatially comprehensive, covering a 10-year period (2 years for PM2. 5) and all non-roadside measurement data from the UK national reference monitor network, which applies consistent operational and QA/QC procedures for each pollutant (44, 47, 24, and 30 sites for NO2, O3, PM2. 5, and PM10, respectively). Two important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – together with root mean square error, were evaluated by site type, year, month, and day-of-week. Model–measurement statistics were generally better than, or comparable to, values that allow for realistic magnitudes of measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2, and PM2. 5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2. 5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at rural background sites (median normalized mean bias (NMB) values for daily O3 and NO2 of 8 and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB = −29 %) and PM2. 5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km × 5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions (e.g. closer to traffic sources) than the grid average. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. some components of wind-blown dust. There were instances of monthly and weekday/weekend variations in the extent of model–measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model–measurement differences (aside from grid versus monitor spatial representivity) was inaccuracy of model emissions – both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals – rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology.