Wintertime PM2 Research Articles

Stable and transport indices applied to winter air pollution over the Yangtze River Delta, China.

Previous studies have developed a stable weather index (SWI) based on meteorological elements that adequately represent PM2.5 pollution over the North China Plain (NCP). However, the SWI performs poorly over the Yangtze River Delta (YRD) region because air pollution over this region is affected not only by stagnant weather (STAG) but also by transport (TRANS). For example, air pollutants can be transported from the NCP to the YRD by cold fronts. In this study, an obliquely rotated principal component analysis in the T-model is applied to classify the synoptic patterns of winter weather over the YRD region from 2013 to 2018. Among the four identified synoptic patterns, two of which cause TRANS, one pattern is most likely to cause STAG, and one pattern can lead to either STAG or TRANS depending on the location of high pressure around Shandong province. Due to the large contribution (63%) of TRANS to the total PM2.5 pollution events, a transport pollution index (TPI) is constructed to describe the transport features of PM2.5 pollution over the YRD region. Our results show that, when considering the SWI alone, the correlation coefficients between the SWI and ln(PM2.5) range from 0.50 to 0.57 in the main cities of the YRD. Excitingly, when considering both the TPI and SWI (TPI+SWI), the correlation coefficients increase significantly to 0.63-0.78, suggesting that TPI+SWI better reflects the wintertime PM2.5 pollution level over the YRD region. In addition, satisfactory performance in validation also suggests that TPI+SWI can increase the accuracy of evaluating and forecasting of PM2.5 pollution episodes over regions downstream of source emissions.

Assessment of the Near-Road (monitoring) Network including comparison with nearby monitors within U.S. cities

Emissions from on-road mobile sources have historically been an important anthropogenic contributor to ambient air pollution leading to high levels of air pollution near major roadways. The U.S. EPA recently implemented the Near-Road (monitoring) Network to measure NO2 concentrations by high-traffic roadways in urban centers throughout the U.S., as these locations were believed to characterize worst-case human exposures to traffic-related air pollutants. Many near-road sites also include PM2.5 and CO measurements, which along with the NO2 observations, were compared in a pairwise manner against non-near-road monitors located within the city-scale boundary. After controlling for primary emissions from the target highways, we found the PM2.5 concentration difference (i.e. near-road concentration minus non-near-road site concentration) between the near-road and non-near-road urban sites to be δ = 0.42 µg m−3( H0: µdiff = 0; Ha: µdiff > 0 (µnon-near-road > µnear-road); p = 0.051; α = 0.05, 95% CI: −0.08–0.90 µg m−3, n = 35 comparisons). NO2 and CO levels were on average higher at the near-road sites compared to the non-near-road urban sites by 5.0 (95% CI: 3.4–6.5) ppb (n = 44 comparisons) and 9.2 × 10−2( 95% CI: 0.04–0.14) ppm (n = 42 comparisons), respectively. The average PM2.5 difference found here is 5%, and at 14 of the 35 (∼40%) urban monitor comparisons and 28 of the 72 (∼39%) overall comparisons, PM2.5 is actually higher at the non-near-road site relative to its near-road pair. Cleaner vehicle fleets, formation of secondary PM from on-road emissions occurring downwind (i.e. away from the road), decreased secondary organic aerosol (SOA) formation rates in the near-road environment, the prevalence of other low-volume vehicular and local, non-vehicular sources of emissions at the non-near-road sites (e.g. railyards, truck yards, ports, biomass-fueled heating, backyard barbecuing, and commercial cooking, etc) and local meteorology (e.g. wind speed and wind direction) explain this finding. The wintertime PM2.5 concentration difference was higher than the other seasons, likely a result of higher primary PM2.5 tailpipe emissions and lower temperatures that both reduced near-road PM volatility and decreased photochemical activity resulting in lower SOA production at the urban scale. Further, all near-road NO2 and CO concentrations were below the annual and hourly NAAQS, while eight (most of which were in wildfire-prone locations) of the 94 PM2.5 sites used in this study were above the annual National Ambient Air Quality Standards. In addition, strong agreement with both annual average daily traffic and fleet-equivalent AADT were found for near-road NO2 and CO concentrations, while weaker, but still positive relationships were found for near-road PM2.5 levels. Lastly, same observational data was used to assess on-road mobile source emission estimates from the EPA National Emission Inventory, and analysis of the observations are in rough agreement with the current ratio of NOx to CO emissions from on-road mobile sources.

Chemical Composition and Light Absorption of PM2.5 Observed at Two Sites near a Busy Road during Summer and Winter

To examine the difference in the major chemical composition of fine particulate matter (PM2.5) between two roadway sites, 24 h integrated PM2.5 samples were simultaneously collected both 15 m (Buk-Ku District Office (BKO) site) and 150 m (Chonnam National University campus (CNU) site) away from busy roads during the summer and winter periods; these samples were taken to determine the concentrations of organic and elemental carbon (OC and EC), water-soluble organic carbon (WSOC), and water-soluble inorganic species. In addition, the real-time aerosol light absorption coefficients (Abs) were measured using a dual-spot seven-wavelength aethalometer at the CNU site to evaluate the influence of traffic and biomass burning (BB) emissions on the concentrations of organic aerosol particles. The hourly NO2 concentration was also observed at an air pollution monitoring network that is about 2 km away from the CNU site. During summer, 24 h PM2.5 concentrations (PM2.5 episode) which exceeded the Korean PM2.5 standard (35 μg/m3) were linked to increases in organic matter (OM) and SO42− concentrations that accounted for on average 35–41% and 26–30%, respectively, of the PM2.5 at the two sites. The increased SO42− concentration was most likely attributable to the inflow of long-range transported aerosols, rather than local production, as demonstrated by both the MODIS (Moderate Resolution Imaging Spectroradiometer) images and transport pathways of air masses reaching the sites. On the other hand, the OM, WSOC, and EC concentrations were directly attributable to traffic emissions at the sampling sites, as supported by the tight correlation between the OC and EC. A small difference between the absorption Ångström exponent (AAE) values calculated at wavelengths of 370–950 nm (AAE370–950nm) and 370–520 nm (AAE370–520nm), and the poor correlation of absorption coefficient by brown carbon (BrC) at 370 nm (AbsBrC370nm) with K+ (R2 = 0.00) also suggest a significant contribution of traffic emissions to OM. However, the wintertime PM2.5 episode was strongly related to the enhanced OM and NO3− concentrations, which contributed 26–28% and 22–23% of the PM2.5 concentration, respectively. It is interesting to note that there were two distinct OC/EC ratios in winter: a lower OC/EC (~3.0), which indicates a significant contribution of traffic emissions to the OC and EC, and a higher OC/EC (~6.5), which suggests an additional influence of BB emissions as well as traffic emissions at the sites. Strong correlations between the OC and EC (R2 = 0.72–0.83) and the enhanced AAE370–520nm values compared to the AAE370–950nm support that BB emissions were also an important contributor to the wintertime OM concentrations as well as traffic emissions at the two sites. A good correlation between the gaseous NO2 and NO3− and meteorological conditions (e.g., low wind speed and high relative humidity) suggest that the heterogeneous oxidation of NO2 on moist particles could be an important contributor to wintertime particulate NO3− formation at the sites. The OC concentrations during summer and winter were higher at the BKO site, with a higher traffic flow and a shorter distance from the roadway than at the CNU site. However, there were slight differences in the concentrations of secondary inorganic species (NO3−, SO42−, and NH4+) between the sites during summer and winter.

Wintertime Particulate Matter Decrease Buffered by Unfavorable Chemical Processes Despite Emissions Reductions in China

AbstractExtreme and persistent haze events frequently occur during wintertime China. While recent emissions reductions reduced annual mean fine particulate matter (PM2.5) concentrations over eastern China, their effectiveness on wintertime PM2.5 trend remains uncertain. We use observations and model simulations to quantify seasonal differences in PM2.5 trends and investigate the underlying chemical mechanisms driving such differences. We find a much slower decrease in observed wintertime PM2.5 (−3.2% yr−1) since 2014, in contrast to a drastic summertime decrease (−10.3% yr−1). Simulations show two previously underappreciated mechanisms buffering wintertime PM2.5 decrease, including an increase in oxidation capacity due to nitrogen oxides (NOx) reductions under wintertime volatile organic compound (VOC)‐limited chemistry, and an enhanced conversion of nitric acid to nitrate by ammonia due to sulfur dioxide reductions. Our findings suggest that control policies targeting VOC and deep NOx reductions are needed to improve wintertime PM2.5 air quality over China.

Effects of atmospheric circulations on the interannual variation in PM<sub>2.5</sub> concentrations over the Beijing–Tianjin–Hebei region in 2013–2018

Abstract. The Chinese government has made many efforts to mitigate fine particulate matter pollution in recent years by taking strict measures on air pollutant reduction, which has generated the nationwide improvements in air quality since 2013. However, under the stringent air pollution controls, how the wintertime PM2.5 concentration (i.e., the mass concentration of atmospheric particles with diameters less than 2.5 µm) varies and how much the meteorological conditions contribute to the interannual variations in PM2.5 concentrations are still unclear, and these very important for the local government to assess the emission reduction of the previous year and adjust mitigation strategies for the next year. The effects of atmospheric circulation on the interannual variation in wintertime PM2.5 concentrations over the Beijing–Tianjin–Hebei (BTH) region in the period of 2013–2018 are evaluated in this study. Generally, the transport of clean and dry air masses and an unstable boundary layer in combination with the effective near-surface horizontal divergence or pumping action at the top of the boundary layer benefits the horizontal or vertical diffusion of surface air pollutants. Instead, the co-occurrence of a stable boundary layer, frequent air stagnation, positive water vapor advection and deep near-surface horizontal convergence exacerbate the wintertime air pollution. Favorable circulation conditions lasting for 2–4 d are beneficial for the diffusion of air pollutants, and 3–7 d of unfavorable circulation events exacerbates the accumulation of air pollutants. The occurrence frequency of favorable circulation events is consistent with the interannual variation in seasonal mean PM2.5 concentrations. There is better diffusion ability in the winters of 2014 and 2017 than in other years. A 59.9 % observed decrease in PM2.5 concentrations in 2017 over the BTH region could be attributed to the improvement in atmospheric diffusion conditions. It is essential to exclude the contribution of meteorological conditions to the variation in interannual air pollutants when making a quantitative evaluation of emission reduction measurements.

Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest.

Soccer games and record-breaking PM<sub>2.5</sub> pollution events in Santiago, Chile

Abstract. In wintertime, high concentrations of atmospheric fine particulate matter (PM2.5) are commonly observed in the metropolitan area of Santiago, Chile. Hourly peaks can be very strong, up to 10 times above average levels, but have barely been studied so far. Based on atmospheric composition measurements and chemistry-transport modeling (WRF-CHIMERE), the chemical signature of sporadic skyrocketing wintertime PM2.5 peaks is analyzed. This signature and the timing of such extreme events trace their origin back to massive barbecue cooking by Santiago's inhabitants during international soccer games. The peaks end up evacuated outside Santiago after a few hours but trigger emergency plans for the next day. Decontamination plans in Santiago focus on decreasing emissions from traffic, industry, and residential heating. Thanks to the air quality network of Santiago, this study shows that cultural habits such as barbecue cooking also need to be taken into account. For short-term forecast and emergency management, cultural events such as soccer games seem a good proxy to prognose possible PM2.5 peak events. Not only can this result have an informative value for the Chilean authorities but also a similar methodology could be reproduced for other cases throughout the world in order to estimate the burden on air quality of cultural habits.

Diagnostic analysis of wintertime PM2.5 pollution in the North China Plain: The impacts of regional transport and atmospheric boundary layer variation

Air pollution has recently become a regional problem in the North China Plain, which is heavily populated with industrialized city clusters. The local air quality is frequently affected by air pollutant transport in this region, as well as other meteorological conditions. This study aims to reveal the roles of air pollutant transport and atmospheric boundary layer variation in the development of air pollution episodes. Two cities (Dezhou and Cangzhou in Shandong and Hebei Provinces, respectively) near the centre of the North China Plain are chosen, and PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations over two winters (January 2016 and December 2017 to January 2018) are evaluated. Both meteorological and air quality observational data are used to construct hourly wind and PM2.5 concentration fields. High-quality atmospheric boundary layer soundings provide detailed information on boundary layer height and its diurnal evolution. A simple box model is employed to simulate variation in PM2.5 levels in these two cities by using the spatial gradients and temporal variation rates in the diagnostic fields of wind and PM2.5 concentrations. Two prominent factors affecting PM2.5 concentration variation are identified by comparing the simulated and observed diurnal variation in PM2.5 concentration. The first one is the entrainment of upper-layer air pollutants into the boundary layer in the morning. And the second one is the temporary accumulation of PM2.5 during the day-to-evening period. After incorporating these two effects, the simulated PM2.5 is comparable with the observed data. The correlation coefficients between the simulation and observation are 0.84 and 0.77 (P < 0.01) for Cangzhou and Dezhou, respectively, and the normalized mean biases are −0.04 and −0.09, respectively. The relative contributions of different processes affecting PM2.5 concentrations during air pollution episodes are assessed with the model simulations. The percentage contributions for local emissions, deposition, horizontal advection, temporary accumulation, and boundary layer height variation are from 27% to 45%, −28% to −20%, 46% to 76%, 13% to 40%, and −6% to 22%, respectively. Local emission and regional air pollutant transport are the main causes of these episodes. Temporary accumulation of PM2.5 during the day-to-evening period is also an important contributor to the episodes. In addition, the entrainment shows varying contributions to PM2.5 increase with a maximum rate of 30% during the PM2.5 pollution episodes in the North China Plain.

China's emission control strategies have suppressed unfavorable influences of climate on wintertime PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; concentrations in Beijing since 2002

Abstract. Severe wintertime PM2.5 pollution in Beijing has been receiving increasing worldwide attention, yet the decadal variations remain relatively unexplored. Combining field measurements and model simulations, we quantified the relative influences of anthropogenic emissions and meteorological conditions on PM2.5 concentrations in Beijing over the winters of 2002–2016. Between the winters of 2011 and 2016, stringent emission control measures resulted in a 21 % decrease in mean mass concentrations of PM2.5 in Beijing, with 7 fewer haze days per winter on average. Given the overestimation of PM2.5 by the model, the effectiveness of stringent emission control measures might have been slightly overstated. With fixed emissions, meteorological conditions over the study period would have led to an increase in haze in Beijing, but the strict emission control measures have suppressed the unfavorable influences of the recent climate. The unfavorable meteorological conditions are attributed to the weakening of the East Asia winter monsoon associated particularly with an increase in pressure associated with the Aleutian Low.

Dominance of large-scale atmospheric circulations in long-term variations of winter PM10 concentrations over East Asia

Concentrations of wintertime particulate matters of diameters below 10 μm (PM 10 ) in South Korea and China have decreased since the 2000s largely owing to the emissions reduction policies of the two countries; however, this decreasing tendency has been notably weakened, or even been reversed, in recent years. This study examines the influence of large-scale atmospheric circulations on this PM 10 change over East Asia for the winters (December–February) of the 2004/05–2015/16 period using an empirical orthogonal function (EOF) analysis. The first EOF mode, which accounts for 32.7% of the total variance, indicates decreases in PM 10 concentrations until 2012 and thereafter increases in them particularly at most stations in eastern and northeastern China. Regression patterns of meteorological variables with respect to the first EOF time series indicate that the wintertime PM 10 variations over East Asia are greatly influenced by the Ural blocking; the weakening of the Ural blocking after 2014 led to the weakening of cold air flows from the north and provided atmospheric conditions favorable for bad air quality events over East Asia. The second EOF mode, which accounts for 20.1% of the total variance, shows a similar spatial distribution as the linear trend of PM 10 concentrations during the analysis period and would be related to the long-term changes in emissions. Our findings emphasize that the long-term variations in air quality over East Asia are affected primarily by the variations in large-scale atmospheric circulations with secondary contributions from the changes in emissions. • This study examines the influence of atmospheric circulation on PM 10 over East Asia. • The first EOF mode shows decrease in winter PM 10 until 2012 and increase thereafter. • Weakening of Ural blocking causes favorable condition for bad East Asian air quality. • The second EOF mode shows a similar distribution as the linear trend of winter PM 10 .

Modeling air quality in the San Joaquin valley of California during the 2013 Discover-AQ field campaign

Abstract The San Joaquin Valley (SJV) of California has one of the nation's most severe wintertime PM2.5 pollution problems. The DISCOVER-AQ (Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality) field campaign took place in the SJV from January 16 to February 6, 2013. It captured two PM2.5 pollution episodes with peak 24-h concentrations approaching 70 μg/m3. Using meteorological fields generated from WRFv3.6, CMAQv5.0.2 was applied to simulate PM2.5 formation in the SJV from January 10 through February 10, 2013. Overall, the model was able to capture the observed accumulation of PM2.5 within the simulation period. The model was able to produce increased concentrations of ammonium nitrate and organic carbon, which are two major components of wintertime PM2.5 in the SJV. Comparison to measurements made by aircraft showed that there was general agreement between observed and modeled daytime vertical distributions of selected gas and particulate species, reflecting the adequacy of modeled daytime mixing layer heights. Excess ammonia predicted by the model implied that ammonium nitrate formation was limited by the availability of nitric acid, consistent with observations. Evaluation of the ammonium nitrate diurnal profile revealed that the observed morning increase of ammonium nitrate was also evident from the model. This paper demonstrates that the CMAQ model is able to simulate elevated wintertime PM2.5 formation observed in the SJV during the DISCOVER-AQ 2013 period, which featured both climatic (i.e., 2011–2014 California Drought) and emissions differences compared to a previous large air quality field campaign in the SJV during 1999–2000.

Characteristics and source apportionment of PM2.5 on an island in Southeast China: Impact of sea-salt and monsoon

Abstract To study the combined effects of the East Asian monsoon and ocean emissions on wintertime and summertime PM2.5 in island cities, filter samples were collected simultaneously at four different functional sites. Based on the chemical compositions of PM2.5, and positive matrix factorization (PMF) model analysis, the pollution characteristics and sources were determined. Insignificant differences, and some correlation in PM2.5 reconstructed compositions were found among the sites (Pt-test > 0.05, P > .05), while significant differences, and no correlation, were found between winter and summer (PANOVA .05). There was more serious chloride depletion in summer (0.88 ± 0.05), caused by both significant Cl− depletion and Na+ enrichment, than in winter (0.18 ± 0.10). The concentrations of non-sea-salt-SO42− in winter were close to those in summer, but the sulfate oxidation ratio (SOR) in winter was much lower than that in summer. The results could be explained by the fact that sea-salt-SO42− has an important contribution to secondary inorganic aerosol on island cities. From both air trajectory clustering and PMF analysis, it was found that there was significant aerosol aging and regional transport in the island city during the East Asian monsoon, and that continental air masses control the variation of air pollution in winter, while sea breezes dominate the characteristics of PM2.5 in summer. This study helps to understand the characteristics and source mechanisms of PM2.5 pollution under complex meteorological conditions in island cities.

Retrospective analysis of 2015–2017 wintertime PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; in China: response to emission regulations and the role of meteorology

Abstract. To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (2015–2017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 µm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98–134, 46–101, 32–59 and 19–60 µg m−3, respectively, indicating that the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11–12, 53–96 and 22–40 µg m−3 were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within ±5 µg m−3. The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 µg m−3 for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 µg m−3). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 µg m−3, while emission control measures caused a decrease of 8 µg m−3, and the combined effects still showed a PM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol–meteorology feedbacks in the model can generate large uncertainties in the analysis as well.

Comprehensively assessing the drivers of future air quality in California

In this study we analyze the impact of major drivers of future air quality, both separately and simultaneously, for the year 2035 in three major California air basins: the South Coast Air Basin (SoCAB), the San Francisco Bay Area (SFBA), and the San Joaquin Valley (SJV). A variety of scenarios are considered based on changes in climate-driven meteorological conditions and both biogenic and anthropogenic emissions. Anthropogenic emissions are based on (1) the California Air Resources Board (CARB) California Emissions Projection Analysis Model (CEPAM), (2) increases in electric sector emissions due to climate change, and (3) aggressive adoption of alternative energy technologies electrification of end-use technologies, and energy efficiency measures.Results indicate that climate-driven changes in meteorological conditions will significantly alter day-to-day variations in future ozone and PM2.5 concentrations, likely increasing the frequency and severity of pollution periods in regions that already experience poor air quality and increasing health risks from pollutant exposure. Increases in biogenic and anthropogenic emissions due to climate change are important during the summer seasons, but have little effect on pollutant concentrations during the winter. Results also indicate that controlling anthropogenic emissions will play a critical role in mitigating climate-driven increases in both ozone and PM2.5 concentrations in the most populated areas of California. In the absence of anthropogenic emissions controls, climate change will worsen ozone air quality throughout the state, increasing exceedances of ambient air quality standards. If planned reductions in anthropogenic emissions are implemented, ozone air quality throughout the less urban areas of the state may be improved in the year 2035, but regions such as the SoCAB and the east SFBA will likely continue to experience high ozone concentrations throughout the summer season. Climate change and anthropogenic emissions controls are both found to decrease wintertime PM2.5 concentrations in the SJV, eliminating nearly all exceedances of PM2.5 National Ambient Air Quality Standards (NAAQS) in the year 2035. However, reductions in anthropogenic emissions are unable to fully mitigate the impact of climate change on PM2.5 concentrations in the SoCAB and east SFBA. Thus, while future air quality in the SJV is projected to be improved in the year 2035, air quality in the SoCAB and east SFBA will remain similar or marginally worsen compared to present day levels. Conversely, we find that aggressive adoption of alternative energy technologies including renewable resources, electrification of end-use technologies, and energy efficiency measures can offset the impacts of climate change. Overall, the two main drivers for air quality in 2035 are changes in meteorological conditions due to climate change and reductions in anthropogenic emissions.

Contributions of City-Specific Fine Particulate Matter (PM2.5) to Differential In Vitro Oxidative Stress and Toxicity Implications between Beijing and Guangzhou of China.

Growing literature has documented varying toxic potencies of source- or site-specific fine particulate matter (PM2.5), as opposed to the practice that treats particle toxicities as independent of composition given the incomplete understanding of the toxicity of the constituents. Quantifying component-specific contribution is the key to unlocking the geographical disparities of particle toxicity from a mixture perspective. In this study, we performed integrated mixture-toxicity experiments and modeling to quantify the contribution of metals and polycyclic aromatic hydrocarbons (PAHs), two default culprit component groups of PM2.5 toxicity, to in vitro oxidative stress caused by wintertime PM2.5 from Beijing and Guangzhou, two megacities in China. PM2.5 from Beijing exhibited greater toxic potencies at equal mass concentrations. The targeted chemical analysis revealed higher burden of metals and PAHs per unit mass of PM2.5 in Beijing. These chemicals together explained 38 and 24% on average of PM2.5-induced reactive oxygen species in Beijing and Guangzhou, respectively, while >60% of the effects remained to be resolved in terms of contributing chemicals. PAHs contributed approximately twice the share of the PM2.5 mixture effects as metals. Fe, Cu, and Mn were the dominant metals, constituting >80% of the metal-shared proportion of the PM2.5 effects. Dibenzo[ a, l]pyrene alone explained >65% of the PAH-shared proportion of the PM2.5 toxicity effects. The significant contribution from coal combustion and vehicular emissions in Beijing suggested the major source disparities of toxicologically active PAHs between the two cities. Our study provided novel quantitative insights into the role of varying toxic component profiles in shaping the differential toxic potencies of city-specific PM2.5 pollution.

Alkanes and aliphatic carbonyl compounds in wintertime PM2.5 in Beijing, China

Organic aerosol is one of the dominant components of PM2.5 in megacities. In order to understand the sources and formation processes of aliphatic carbonyl compounds, the concentrations of saturated and unsaturated aliphatic hydrocarbons and carbonyl compounds were determined in the PM2.5 from an urban area of Beijing sampled in November–December 2016 and analysed using two dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC x GC-TOFMS). The data were separated into non-haze and haze days (PM2.5 ≥ 75 μg m−3). n-Alkanes (C10-C36) and n-alkenes (C12-C26) were abundant in the samples during the sampling campaign regardless of non-haze and haze conditions, and the middle chain length n–alkanes (C25–C34) were the most abundant. Aliphatic carbonyls, specifically n-alkanals (C8-C29), n-alkan-2-ones (C8-C31) and n-alkan-3-ones (C8-C30) were also detected in the PM2.5 at concentrations much lower than n-alkanes. The n-alkanals were the most abundant compounds amongst the aliphatic carbonyls, accounting for 65.4% on average of the total mass of aliphatic carbonyls. For the non-haze days, it seems likely that the OH oxidation of n-alkanes was a source of carbonyl compounds, whereas vehicle exhaust makes a significant contribution to the n-alkanes and n-alkanals, but makes a much smaller contribution to the n-alkan-2-ones and n-alkan-3-ones. It appears that primary sources are likely to be a major contributor to concentrations of the high molecular weight carbonyl compounds during the haze episodes, probably deriving from coal combustion. In addition, furanones (γ-lactones) and phytone (6, 10, 14-trimethylpentadecan-2-one) were also detected in our samples and are oxidation products of hydrocarbons and a marker of biogenic input, respectively. Cooking emissions also appear to be a likely contributor to the furanones. This study provides new information on the profiles of PM2.5-associated aliphatic hydrocarbons and carbonyl compounds, and gives insights into their sources. Differences in the concentrations and ratios of carbonyl compounds between Beijing and London are discussed.

A combined Arctic-tropical climate pattern controlling the inter-annual climate variability of wintertime PM2.5 over the North China Plain

In recent years, the Chinese government has made tremendous efforts to reduce the emissions of atmospheric pollutants throughout the country. An apparent improvement in air quality was observed in Beijing and its adjacent region during the winter of 2017/2018. However, caution should be taken in directly attributing this improvement to air control actions without taking the effects of climate variability into account. Here, we develop a statistical prediction model that can successfully predict the variability of wintertime PM2.5 concentrations observed over these regions. Our analysis indicates that the remarkable decrease in PM2.5 concentrations over the North China Plain (NCP) observed during the winter of 2017/2018 can be largely explained by changes in meteorological conditions. To clarify which climate factors control the inter-annual variability of wintertime PM2.5 pollution over the NCP, we further reconstructed a 30-year time series of wintertime PM2.5 levels over the NCP over the period of 1988–2017 using our statistical model. Through our analysis, we found that the combined Arctic-tropical climate effects related to the ENSO and Arctic warming controlled the inter-annual variability of wintertime PM2.5 over the NCP. Specifically, the rapid warming of the Barents-Kara Sea region enhances the Siberian High and thus plays an important role in improving the air quality over the NCP during the 2017/2018 wintertime. These results help us understand the role of climate variability in modulating air quality, especially its contributions to the winter of 2017/2018. These results may assist in the evaluation of current air control actions and the revision of relevant policy for the future, which are urgently needed for China.

Ambient wintertime particulate air pollution and hypertensive disorders of pregnancy in Monroe County, New York

BackgroundPrevious studies have reported associations between ambient fine particle (PM2.5) concentrations and hypertensive disorders of pregnancy (HDP). However, none have examined whether ultrafine particles (UFP; < 100 nm), accumulation mode particles (AMP; 100–500 nm), markers of traffic pollution (black carbon; BC), or wood burning (Delta-C; (30% of ambient wintertime PM2.5 in Monroe County, NY is from wood burning)) are associated with an increased odds of HDP. We estimated the odds of HDP associated with increased concentrations of PM2.5, UFP, AMP, BC, and Delta-C in each gestational month during winter months. MethodsElectronic medical records and birth certificate data were linked with land-use regression models in Monroe County, New York in 2009–2013 to predict monthly pollutant concentrations during winter (November-April) based on maternal residential address for 16,637 births. Using multivariable logistic regression, we estimated the odds of HDP associated with each interquartile range (IQR) increase in PM2.5, UFP, AMP, BC, and Delta-C concentrations during each gestational month, adjusting for maternal characteristics, birth hospital, temperature, and relative humidity. ResultsEach 0.52 µg/m3 increase in Delta-C concentration during the 7th gestational month was associated with an increased odds of HDP (odds ratio (OR) = 1.21; 95% confidence interval (CI) = 1.01, 1.45), with a similar sized estimate in month 8 (OR = 1.18; 95%CI = 0.98, 1.43). Non-statistically significant increased odds of HDP associated with IQR increases in BC concentrations during months 3 (OR = 1.12; 95%CI = 0.98, 1.28) and 7 (OR = 1.12; 95%CI = 0.96, 1.29) were observed. Increased odds of HDP were not observed for PM2.5, UFP, or AMP. ConclusionsOur findings suggest that maternal exposure to wood smoke in Monroe County during winter is associated with an increased odds of HDP during late gestation. Additional studies are needed to evaluate the effect of wood smoke on HDP and to explore effects on other pregnancy outcomes.

Comparing the impact of strong and weak East Asian winter monsoon on PM2.5 concentration in Beijing

We investigate the relationship between the wintertime PM2.5 concentration in Beijing from 2005 to 2016 and the East Asian winter monsoon (EAWM) strength based on PM2.5 observations and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. The large-scale atmospheric circulations are compared and the differences in spatial distribution and source areas of Beijing PM2.5 concentration are studied in depth combined with GRAPES-CUACE atmospheric chemistry model and its aerosol adjoint model in strong and weak EAWM years. The results show that the monthly Beijing PM2.5 concentration in winter is significantly anti-correlated with the individual EAWM indices of winter month. According to three EAWM indices, December 2014 and 2016 are selected as the representatives of strong and weak EAWM years for further comparison. In December 2014, the strong EAWM circulation was characterized by the strengthened subtropical westerly jet stream at 300 hPa, the deepened 500 hPa East Asian trough, the strengthened northwesterly wind at 850 hPa in North China, and the developed Siberian High. Conversely, these anomalous patterns in December 2016 were much weaker, resulting in lower boundary layer height, more stable atmosphere stratification and more frequent inversion. The simulated PM2.5 concentration in the northern and southern part of Beijing were affected by the EAWM to different extents, with the values of 55–75 μg/m3 in the north and 95–125 μg/m3 in the south. There was no obvious difference in the locations of sensitive source areas between December 2014 and 2016. However, compared to December 2014, the accumulated sensitivity coefficients of Beijing, Tianjin, Hebei and Shanxi sources at 72 h ahead of the objective time period were 2.2, 5.9, 1.6, and 1.4 times higher, and the key discharge period was 2.6 times longer in December 2016. The contributions of Beijing and Tianjin sources had greater increase rates, stressing the importance of sensitive source areas in the direction of southeast Beijing in weak EAWM year.

Winter haze over North China Plain from 2009 to 2016: Influence of emission and meteorology

Analysis of observed PM2.5 in Beijing since 2009 reveals that winter haze over North China Plain (NCP) peaked in 2012 and 2013 and there was an improvement in air quality until 2016. The variation of wintertime PM2.5 from 2009 to 2016 is influenced by both emission changes and meteorology conditions, and we quantified the relative contributions from these two aspects. Sensitivity simulation by GEOS-Chem suggested that emission reductions over NCP in 2013–2017 caused 10% decrease of regional mean PM2.5 concentration in 2016 winter compared to 2012 winter level. We removed emission influence on PM2.5 to get PM2.5 that influenced by meteorology (met-influenced PM2.5). For met-influenced PM2.5, compared to original-observed PM2.5(the US Embassy data), percentage of clean days (daily PM2.5 ≤ 75 μg m−3) decreases while that of polluted (75 μg m−3 < daily PM2.5 ≤150 μg m−3) and heavily polluted (150 μg m−3 < daily PM2.5 ≤ 250 μg m−3) days increases. However, proportion of extremely polluted (daily PM2.5 > 250 μg m−3) days stays unchanged, even if emission reduction is doubled, indicating that the extremely polluted situation over NCP is dominated by meteorological conditions, and emission control from 2013 to 2017 has little effects on the extremely polluted days. We developed an effective haze day index (HDI) to represent the weather conditions conducive to haze days. HDI is constructed based on the normalized near surface meridional wind (V850), temperature difference (δT) between near surface (850 hPa) and upper atmosphere (250 hPa), and the relative humidity at 1000 hPa (RH1000). HDI is skillful to detect 72% of the severe haze days (daily PM2.5 > 150 μg m−3). On average, the anomalously high V850 is the main cause of severe haze, while in 2012 winter, RH1000 favorable for secondary aerosols’ formation is the largest contributor to haze.