Winter PM2 Research Articles

Statistical Seasonal Forecasting of Winter and Spring PM2.5 Concentrations Over the Korean Peninsula.

Concentrations of fine particulate matter smaller than 2.5 μm in diameter (PM2.5) over the Korean Peninsula experience year-to-year variations due to interannual variation in climate conditions. This study develops a multiple linear regression model based on slowly varying boundary conditions to predict winter and spring PM2.5 concentrations at 1–3-month lead times. Nation-wide observations of Korea, which began in 2015, is extended back to 2005 using the local Seoul government’s observations, constructing a long-term dataset covering the 2005–2019 period. Using the forward selection stepwise regression approach, we identify sea surface temperature (SST), soil moisture, and 2-m air temperature as predictors for the model, while rejecting sea ice concentration and snow depth due to weak correlations with seasonal PM2.5 concentrations. For the wintertime (December–January–February, DJF), the model based on SSTs over the equatorial Atlantic and soil moisture over the eastern Europe along with the linear PM2.5 concentration trend generates a 3-month forecasts that shows a 0.69 correlation with observations. For the springtime (March–April–May, MAM), the accuracy of the model using SSTs over North Pacific and 2-m air temperature over East Asia increases to 0.75. Additionally, we find a linear relationship between the seasonal mean PM2.5 concentration and an extreme metric, i.e., seasonal number of high PM2.5 concentration days.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13143-022-00275-4.

Rutin ameliorates the promotion effect of fine particulate matter on vascular calcification in calcifying vascular cells and ApoE-/- mice

Atmospheric PM2.5 exposure greatly contributes to the incidence of and mortality from cardiovascular disease (CVD). Owing to the crucial role of vascular calcification in the progression of CVD, it is imperative to elucidate the effects of PM2.5 on vascular calcification to understand the toxic mechanisms of haze-induced CVD. However, the effects of PM2.5 exposure on vascular calcification and the underlying molecular mechanisms are still unclear. In this work, the in vitro and in vivo models were used to illuminate the effects of PM2.5 on vascular calcification. We found that PM2.5 promoted the deposition of hydroxyapatite in calcifying vascular cells. Moreover, hydroxyapatite deposition was significantly enhanced by 3.5 times compared with those in the control group in aortas of ApoE-/- mice after exposure winter PM2.5 (1.5 mg/kg b.w.), accompanied by activation of the OPG/RANKL pathway and inflammatory cytokines’ expressions. Moreover, PM2.5-induced reactive oxygen species (ROS) generation was observed. NAC, an ROS inhibitor, observably alleviated the promotion effects of PM2.5 on vascular calcification. Furthermore, rutin effectively prevented vascular calcification by regulating the OPG/RANKL pathway. Our results suggest that PM2.5 play an important role in the occurrence and development of vascular calcification, and that rutin has an antagonistic effect on it.

Decisive role of ozone formation control in winter PM2.5 mitigation in Shenzhen, China

During the COVID-19 lockdown, atmospheric PM2.5 in the Pearl River Delta (PRD) showed the highest reduction in China, but the reasons, being a critical question for future air quality policy design, are not yet clear. In this study, we analyzed the relationships among gaseous precursors, secondary aerosols and atmospheric oxidation capacity in Shenzhen, a megacity in the PRD, during the lockdown period in 2020 and the same period in 2021. The comprehensive observational datasets showed large lockdown declines in all primary and secondary pollutants (including O3). We found that, however, the daytime concentrations of secondary aerosols during the lockdown period and normal period were rather similar when the corresponding odd oxygen (Ox≡O3+NO2, an indicator of photochemical processing avoiding the titration effect of O3 by freshly emitted NO) were at similar levels. Therefore, reduced Ox, rather than the large reduction in precursors, was a direct driver to achieve the decline in secondary aerosols. Moreover, Ox was also found to determine the spatial distribution of intercity PM2.5 levels in winter PRD. Thus, an effective strategy for winter PM2.5 mitigation should emphasize on control of winter O3 formation in the PRD and other regions with similar conditions.

Air stagnation in China: Spatiotemporal variability and differing impact on PM2.5 and O3 during 2013–2018

In recent years, winter PM2.5 and summer O3 pollution which often occurred with air stagnation condition has become a major concern in China. Thus, it is imperative to understand the air stagnation distribution in China and elucidate its impact on air pollution. In this study, three air stagnation indices were calculated according to atmospheric thermal and dynamics parameters using ERA5 data. Two improved indices were more suitable in China, and they displayed similar characteristics: most of the air stagnant days were found in winter, and seasonal distributions showed substantial regional heterogeneity. During stagnation events, flat west or northwest winds at 500 hPa and high pressure at surface dominated, with high relative humidity (RH) and temperature (T), weak winds in most regions. The pollutants concentrations on stagnant days were higher than those on non-stagnant days in most studied areas, with the largest difference of the 90th percentiles of maximum daily 8-h average (MDA8) O3 up to 62.2 μg m−3 in Pearl River Delta (PRD) and PM2.5 up to 95.8 μg m−3 in North China Plain (NCP). During the evolution of stagnation events, the MDA8 O3 concentrations showed a significant increase (6.0 μg m−3 day−1) in PRD and a slight rise in other regions; the PM2.5 concentrations and the frequency of extreme PM2.5 days increased, especially in NCP. Furthermore, O3 was simultaneously controlled by temperature and stagnation except for Xinjiang (XJ), with the average growth rate of 19.5 μg m−3 every 3 °C at 19 °C–31 °C. PM2.5 was dominated by RH and stagnation in northern China while mainly controlled by stagnation in southern China. Notably, the extremes of summer O3 (winter PM2.5) pollution was most associated with air stagnation and T at 25 °C–31 °C (air stagnation and RH >50%). The results are expected to provide important reference information for air pollution control in China.

Planetary Boundary Layer and its Relationship with PM2.5 Concentrations in Almaty, Kazakhstan

Air pollution is a severe problem in Almaty (Kazakhstan), especially during the cold half of the year (October-March). Almaty is one of the most polluted cities in Kazakhstan and Central Asia, with average winter PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) concentrations higher than 100 μg m-3. High pollution in the wintertime in Almaty could be caused by emissions from coal combustion for power and heat generation (at power plants and small-scale heating), which could also be worsened by poor dispersion of air pollutants due to certain atmospheric conditions. Based on one-year radiosonde data, the characteristics of the planetary boundary layer height (PBLH) and its effect on ground-level PM2.5 concentrations in Almaty were analyzed in this study using the bulk Richardson number (Ri) and potential temperature increase (PT) methods. During an annual cycle, the concentrations of PM2.5 were highest in the winter months when the daily concentrations were above 100 μg m-3 for 39 days during this period. The results show a clear negative relationship between the daily average PM2.5 concentrations and PBLH at 12.00 UTС. For instance, high PM2.5 concentrations in winter months (94.0 μg m-3) corresponded to a lower PBLH (393 m), and low PM2.5 concentrations in summer months (9.9 μg m-3) corresponded to a higher PBLH (1970 m). During the cold half of the year, the top 20% of PM2.5 concentrations were associated with a lower PBLH and calm wind conditions (lower average wind speeds within the PBL and a lower ventilation coefficient). The results show that PBLH variations during the year have a significant effect on PM2.5 concentrations; however, further analysis is needed with a more substantial amount of observational data to understand this interaction further and to investigate the role of synoptic processes that lead to a shallow PBLH.

In-vitro oxidative potential and inflammatory response of ambient PM2.5 in a rural region of Northwest China: Association with chemical compositions and source contribution

Overproduction of reactive oxygen species (ROS) induced by atmospheric particles and subsequent inflammatory responses are considered as one of the most important pathological mechanisms with regard to the adverse effects of air pollution exposure. In this study, fine particulate matter (PM2.5) samples were collected at a rural site in Guanzhong Basin, Northwest China, in both summer (August 3–23, 2016) and winter (January 5–February 1, 2017). Then, human bronchial epithelial BEAS-2B cells were exposed to the PM2.5, cultured for 24 h, and then assayed for particle-induced ROS and three inflammatory factors (tumor necrosis-α (TNF-α), interleukin-6 (IL-6), and interferon-γ (IFN-γ)) in vitro. The oxidative potential (OP) induced by winter PM2.5 samples was higher than that induced by summertime samples, whereas inflammatory values showed contrasting seasonal variations. Both OP and inflammatory factors were significantly correlated with most chemical compounds in winter, but not in summer, which was thought to be related mainly to the higher contribution from secondary aerosols formed during the warm season. Source apportionment results showed secondary aerosols formation have significant contribution to OP of PM2.5 in both seasons, but the dominant oxidation processes is different. Secondary nitrates-related process was the major contributors regulating the OP in winter; however, secondary sulfates formation were mainly responsible for the ROS responses in summer. For primary emission, biomass burning, rather than coal emission or vehicle exhaust, was the significant source for OP of PM2.5 in winter. In most cases, the source contribution of each inflammatory factor was similar to that of the ROS. Our results highlighted the significant health risk of atmospheric aerosols from biomass burning in the rural regions of Guanzhong Basin, Northwest China.

Characteristics of Atmospheric Particulate Matter Pollution and the Unique Wind and Underlying Surface Impact in the Twain-Hu Basin in Winter

To understand the recent characteristics of atmospheric environmental changes in the Twain-Hu(Hunan-Hubei) Basin, including the middle reaches of the Yangtze River, this paper uses near-surface PM2.5 and PM10 observational data for the Twain-Hu Basin in the winters of 2015 to 2019, combined with wind-speed, topography, the normalized difference vegetation index(NDVI), and other datasets. The results show that:① PM2.5 pollution occurred frequently in the winters of 2015-2019 in the Twain-Hu Basin, and Xiangyang and Jingmen in the western part of the basin, experience PM2.5 pollution on an average of 62 and 61 days in winter(PM2.5>75 μg·m-3). And the heavy pollution days in Xiangyang reached 19 more days(PM2.5>150 μg·m-3), indicating that the Twain-Hu Basin is an air pollution center in the middle reaches of the Yangtze River Basin; ② Spatially, pollution in the Twain-Hu Basin is heavier in the northwest than in the southeast, and in the urban agglomeration, which is mainly related to the regional transport of air pollutants by the winter monsoon and the high levels of emissions from urban areas; ③ A "U-shaped" nonlinear relationship was observed between near-surface wind speeds and PM2.5 and PM10 concentrations. The inflection points of PM2.5 and PM10 concentrations were 153 and 210 μg·m-3, respectively. This implies that the accumulation of local atmospheric particulate matter in the Twain-Hu Basin dominates light/medium pollution, while the regional transport of air pollutants dominates period of severe pollution; and ④ PM2.5 and PM10 in winter were significantly negatively correlated with terrain height and the NDVI, which reflects the atmospheric environmental effects of topography and urbanization.

Morphological Compared Study of PM<sub>2.5 </sub>Particles in Air Pollution of Ulaanbaatar

In this study, we report a change of morphological distribution for PM2.5 air pollution in Ulaanbaatar. Comparing the measurement results in 2011 and 2017 the concentration of particle size 0.8μm was decreased from 92% to 76.7%. Among winter samples in 2011 it is identified 0.4-0.8μm sized particles covering 48.8% of total, and for samples in 2017 it is 61.7%. This shows that 0.4-0.8 microns of particulate matter predominate in the air pollution caused by solid fuel combustion. The 66.8% of the particles identified <0.8μm sized are in summer samples in 2011. This percentage has increased up to 94.5% in the samples in 2017. It is having been to the most harmful fraction of particulate matters for human health. About particulate shapes distribution, irregular shapes dominated in winter in 2011 and sphere shapes dominated in summer samples. While irregular shapes dominated in the samples in 2017, it was independent of season. This provides on origin of pollution, for example, the summer PM2.5 pollution is sphere shape from the soil in summer and winter PM2.5 pollution is irregular from the solid fuel combustion. This is a pilot compared study of PM2.5 particles in air pollution of Ulaanbaatar.

The convergence of PM2.5 concentration in Chinese cities: a distribution dynamic approach

To fill the gap in the research on the convergence trend of air pollutants since 2013 in China and overcome the Galton fallacy caused by the parametric regression method, this study examines the convergence trend of the annual average concentration of fine particulate matter 2.5 (PM2.5) in China’s prefecture-level cities after 2013 using a distribution dynamic approach. The winter PM2.5 pollution in Chinese cities is severe. Hence, the convergence of the average winter PM2.5 concentration of prefecture-level cities is also explored in this study. The results show that during 2015–2019, the annual average PM2.5 concentration level improved significantly. However, the average PM2.5 winter concentration level in 2015–2018 did not significantly decrease, with some cities showing severe pollution levels. The annual average PM2.5 of China's prefecture-level cities exhibit club convergence, while the PM2.5 concentration in winter exhibits ‘unikurtosis’. In the long run, the annual average PM2.5 clusters around two levels, at approximately 35 μg/m3 and 60 μg/m3, while the average PM2.5 in winter is concentrated at 100 μg/m3. In the long run, in the central region, PM2.5 pollution is more severe than in northern and southern areas, regardless of the annual or winter average PM2.5 concentration.

The effect of PM2.5-related hazards on biomarkers of bronchial epithelial cells (A549) inflammation in Karaj and Fardis cities.

Fine particles (especially PM2.5 particles) in ambient air can cause irreversible effects on human health. In the present study, seasonal variations in toxicity PM2.5 (cell viability and release of pro-inflammatory cytokines) were exposed human lung cells (A549) to concentrations of PM2.5 samples in summer (sPM2.5) and winter (wPM2.5) seasons. Cells were separately exposed to three concentrations of PM2.5 (25, 50, and 100 μg/mL) and three times (12 h, 1 and 2 days). We evaluated cell viability by MTT assay [3- (4, 5-dimethylthiazol-2-yl) -2, 5-diphenyltetrazolium bromide] and liberation of pro-inflammatory cytokines (interleukin-6 and interleukin-8) by the ELISA method. The toxicological results of this study showed that increasing the concentration of PM2.5 particulates and contact time with it reduces cell viability and increases inflammatory responses. Seasonal cytotoxicity of PM2.5 particles in high-traffic areas at summer season compared to winter season was lower. The lowest percent of viability at 2 days of exposure and 100 μg/mL exposure in the winter sample was observed. Also, PM2.5 particles were influential in the amount of interleukins 8 and 6. The average release level of IL-6 and IL-8 in the cold season (winter) and the enormous exposure time and concentrations (2 days-100 μg/mL) was much higher than in the hot season (summer). These values were twice as high for winter PM2.5 samples as for summer samples. The compounds in PM2.5 at different seasons can cause some biological effects. The samples' chemical characteristics in two seasons displayed that the PMs were diverse in chemical properties. In general, heavy metals and polycyclic aromatic hydrocarbons were more in the winter samples. However, the samples of wPM2.5 had a lower mass quota of metals such as aluminum, iron, copper, zinc, and magnesium. Concentrations of chromium, cadmium, arsenic, mercury, nickel, and lead were more significant in the sample of wPM2.5.

Meteorological Impact on Winter PM2.5 Pollution in Delhi: Present and Future Projection Under a Warming Climate

AbstractThis study employs the self‐organizing map classification to track wintertime PM2.5 pollutions in Delhi under four atmospheric circulation patterns during 2013–2020. We found that the most polluted circulation pattern was characterized by a northward shift in the subtropical jet stream and a northward intrusion of the subtropical high, leading to descending anomalies from the upper troposphere to the near surface. Together, the resultant reduced passage of cyclones, lower planetary boundary layer, weakened near‐surface winds and less precipitation contributed to wintertime PM2.5 pollution in Delhi. Compared with the 1981–2010 mean levels, Coupled Model Intercomparison Project 6 models project Delhi would experience two fewer days of severe pollution‐favorable circulation pattern and seven more days of clean‐favorable circulation pattern in 2070–2099 under the shared socioeconomic pathway 5–8.5. Future decrease in the severe pollution‐favorable circulation pattern may be attributed to the warming trends in sea surface temperature over the central Pacific and North Atlantic Ocean.

Rethinking the causes of extreme heavy winter PM2.5 pollution events in northern China

It has been reported that air quality models largely underestimate PM2.5 concentrations during severe pollution events in China. In this study, the Models-3 Community Multi-scale Air Quality model (CMAQ) was employed to simulate PM2.5 concentrations in May–June (non-heating period) and in November–December (heating period) of 2013 in northern China, with a particular focus on determining the causes of the underestimation. Modeling results reproduced the mass concentrations of PM2.5 in approximately 50% of the non-heating and heating periods in Qingdao (referred to as the good periods), while the model performance was unsatisfactory during the remaining periods (the poor periods). In this respect, the overprediction of inorganic salts and the underprediction of organic matter in PM2.5 canceled each other out and resulted in a good simulation of PM2.5 concentrations during the good periods, whereas during poor periods, the bias of the planetary boundary layer height, wind direction, precipitation, and other factors caused inconsistencies between the simulated and observed PM2.5 concentrations. Sensitivity studies showed that the underestimation of primarily emitted particles from local emissions was likely the main cause of PM2.5 underpredictions during heavy haze days. Furthermore, our results implied that the assumption of the conditions of the gas–aerosol thermodynamic equilibria in the air quality model likely results in an overprediction of secondary PM2.5 inorganic salts (SO42− + NO3− + NH4+) during clear days. In contrast, during heavy pollution or heavy haze days, high concentrations of air pollutants theoretically rapidly leads to gas/particle chemical equilibrium and no overprediction of SO42−, NO3−, and NH4+ concentrations. Nevertheless, the underestimation of primarily emitted particles from local sources during heavy haze days is yet to be explained and needs further investigation.

Atmospheric PM2.5-bound polycyclic aromatic hydrocarbons in China's four cities: Characterization, risk assessment, and epithelial-to-mesenchymal transition induced by PM2.5

The chemical composition of particulate matter (PM) in urban area is prominent for the human health and generally takes a clear regional difference character. Among the complex components of PM, polycyclic aromatic hydrocarbons (PAHs) are deserved to be specifically investigated from the reason of their high carcinogenic ability. In this study, PM2.5 samples were collected from Taiyuan, Beijing, Hangzhou, and Guangzhou in four seasons, and the concentrations of 18 PAHs were measured and analyzed. The results indicated that PAHs concentrations were significantly higher in PM from Taiyuan and Beijing than those from Hangzhou and Guangzhou. According to the health risk assessment using incremental lifetime cancer risk (ILCR) model, the cancer risk of PAHs of PM from Taiyuan was the highest, and the risk values of PAHs of PM in winter of four cities exceeded the WHO health-based guideline value. In vitro experiments indicated that winter PM2.5 from four cities induced varying degrees of migration and invasion in A549 cells. In vivo experiments indicated that winter PM2.5 from four cities triggered altered expressions of Epithelial-to-mesenchymal Transition (EMT) markers in C57BL/6 mice.

Aggravation effect of regional transport on wintertime PM2.5 over the middle reaches of the Yangtze River under China's air pollutant emission reduction process

The contributions of emission reduction and meteorological changes to air quality improvement in Hubei Province (HB) have required assessment in recent years. In this study, the WRF-Chem scenario tests were conducted to evaluate the effect of both the emission and meteorological changes on the winter PM2.5 from 2015 to 2019, especially that of regional transport and local emissions on the PM2.5 variations in HB. The results showed that meteorological changes in January 2019 increased the PM2.5 by 24% relative to January 2015, whereas emissions reduced it by 36%, indicating that emission reduction was vital in improving the air quality in HB. However, the meteorological changes increased the concentration of air pollutants by 10–25% relative to January 2018. This led to a rebound of the observed pollutant concentrations in January 2019, highlighting the importance of meteorological conditions on air pollution. Regional transport of air pollutants contributed to the inter-annual increase of wintertime PM2.5 by 78%, driven purely by meteorology from 2015 to 2019. This indicated that regional PM2.5 transport could aggravate PM2.5 levels in winter over HB with the reduction of air pollutant emissions in China. Additionally, the regional transport of air pollutants contributed to the decrease in PM2.5 in HB by 42%, resulting from the non-local emission reduction during COVID-19 lockdown in winter. This indicates the importance of the regional transport of air pollutants in driving regional changes in the atmospheric environment over China.

Seasonal and areal variability in PM2.5 poses differential degranulation and pro-inflammatory effects on RBL-2H3 cells

PM2.5 pollution is a widespread environmental and health problem, particularly in China. Besides leading to well-known diseases in the respiratory system, PM2.5 can also alter immune function to induce or aggravate allergic diseases. To determine whether there are temporal and spatial differences in the allergic responses to PM2.5, monthly samples were collected from four regions (urban, industrial, suburban, and rural areas) through a whole year in Nanjing city, China. Inorganic chemical components (metals and water-soluble ions) of PM2.5 were analyzed, and the rat basophil cells (RBL-2H3) exposed to PM2.5 were assessed through quantitative measures of degranulation (β-hex and histamine) and pro-inflammation cytokine (IL-4 and TNF-α) expression. The highest levels of β-hex were measured in winter and spring PM2.5 from urban and industrial areas, or autumn PM2.5 from suburban and rural areas. With respect to histamine, autumn PM2.5 samples were most potent irrespective of the location. Autumn and winter PM2.5 induced higher levels of IL-4 than spring and summer samples. However, spring and autumn PM2.5 caused higher levels of TNF-α. The concentrations of water-soluble ions (NH4+, K+ and Cl−), as well as heavy metals (Pb and Cr), were directly and statistically correlated to the inflammation observed in vitro. In general, the differences between regional and seasonal PM2.5 in stimulating cell degranulation may depend on endotoxin and airborne allergen content of PM2.5. The heavy metals and water-soluble ions in PM2.5 were mostly anthropogenic, which increased the particles' mass-based cellular inflammatory potential, therefore, their health risks, e.g. from vehicular exhaust, coal, and biomass combustion, cannot be ignored.

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China.

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%–94% of first‐order sensitivity index), industrial (7%–31%), and agricultural emissions (0%–24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%–81%, down to 15.3–25.9 μg m−3, remaining above the World Health Organization annual guideline of 10 μg m−3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m−3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors.

Insights into atmospheric oxidation capacity and its impact on PM2.5 in megacity Beijing via volatile organic compounds measurements

This study conducted a VOCs monitoring campaign in Jul and Oct of 2018, Jan and Apr of 2019 in Beijing urban area, to explore the atmospheric oxidation capacity and its impact on PM2.5 there through investigating the chemical behaviors of VOCs at a 1-h time resolution. We totally detected 53 VOCs in the atmosphere over Beijing, sum of them (TVOCs) reaching 29.0 ± 12.9, 47.6 ± 29.4, 45.7 ± 56.4 and 35.8 ± 19.3 ppb (mol/mol) in Jul, Oct, Jan and Apr respectively. The gap between VOCs and CO diurnal amplitudes presented the trend of Jul > Oct and Apr > Jan, which implies the more chemical loss of VOCs in non-winter seasons compared to winter. Then, according to the notable difference in OH- and O3- reactivity of various VOCs, hourly OH exposure ([OH]Δt), O3 exposure ([O3]Δt) and OH concentration were estimated. In result, in Jul, Oct, Jan and Apr, the daily [OH]Δt was 10.39 ± 4.95, 11.10 ± 6.01, 6.39 ± 3.74 and 8.96 ± 5.05 × 1010 molecules·s·cm−3, the nighttime [O3]Δt was 9.43 ± 2.83, 5.22 ± 3.62, 5.79 ± 3.65 and 8.60 ± 4.74 × 1015 molecules·s·cm−3, and the daily OH was 9.76 ± 5.17, 5.11 ± 2.98, 2.95 ± 2.18 and 6.26 ± 3.18 × 106 molecules·cm−3, in respective. The OH peak magnitudes agreed well with the OH measured in-site, which indicates the reliability of our estimates on the oxidation capacity of atmosphere over Beijing. During our studied periods, [OH]Δt, OH and atmospheric oxidability combining OH and O3 (ACO) were all positively related to PM2.5 in Jul and Apr; only [OH]Δt was positively related to PM2.5 in Oct; while [OH]Δt, OH and ACO were all negatively related to PM2.5 in Jan. The proportion of SO2 and VOCs oxidized through gaseous OH reactions was found to increase with PM2.5 increasing in non-winter seasons, but decrease with PM2.5 increasing in winter. It means that high PM2.5 concentrations are attributed more to gas-phase oxidations in non-winter seasons and potentially boosted by photochemistry in summer and spring. But, in winter high PM2.5 concentrations effectively inhibit photolysis reactions and weaken atmospheric oxidation capacity, which makes high PM2.5 less dependent on the gas-phase oxidations.

Impact of Arctic Oscillation anomalies on winter PM2.5 in China via a numerical simulation.

The impact of Arctic Oscillation (AO) anomalies on winter PM2.5 variability in China was investigated using a numerical modeling system (WRF-CUACE). The model results showed that the influence of AO anomalies on winter PM2.5 concentration was mainly concentrated in eastern China, especially in Central China (CEN), Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and Pearl River Delta (PRD) and was mostly consistent with the conclusions of a previous analysis using haze data. Winter PM2.5 concentrations in CEN and BTH increased under abnormally high AO and decreased under abnormally low AO due to the subsequent changes in specific meteorological conditions, such as temperature, wind speed, and boundary layer height. Winter PM2.5 decreased in the YRD and PRD in both abnormally high and low AO years due to more favorable vertical transport conditions and regional transport capacity compared with those of other regions. In addition to meteorological factors, AO anomalies also impacted PM2.5 depositions in winter, with more apparent effects in southern China. It is found that AO had a larger impact on dry deposition than on wet deposition, and dry deposition was a dominant factor affecting PM2.5 concentrations in CEN.

Characteristics and Sources of Water-soluble Ion Pollution in PM2.5 in Winter in Shenyang

In order to explore the characteristics of PM2.5 and water-soluble ions in Shenyang in winter, the URG-9000D online monitoring system was used to continuously sample PM2.5 and gas components during 2018. The results indicated that the average concentration of PM2.5 in Shenyang during the sampling period was 80.67 μg·m-3, and the total water-soluble ion concentration ranged from 2.68 to 132.79 μg·m-3. Compared with clean days, the proportion of NO3-, SO42-, and NH4+ (SNA) in polluted days increased significantly, reached 43.7% of PM2.5. The rapid accumulation of SO2 in a short period of time made atmospheric PM2.5 explosively increase in Shenyang in winter. A Pearson correlation analysis showed that the correlation coefficients of SNA, Cl-, and PM2.5 were all above 0.78, indicating that the main contribution components of winter PM2.5 in Shenyang were SNA and Cl-. The apportionment of PMF sources indicated that the sources of pollutants in winter in Shenyang mainly included secondary reaction sources, coal and biomass combustion sources, and dust sources.

Assessment of Machine Learning Algorithms in Short-term Forecasting of PM10 and PM2.5 Concentrations in Selected Polish Agglomerations

Air pollution continues to have a significant impact on Europeans living in urban areas, and episodes of elevated PMx are responsible for a large number of premature deaths (mostly due to heart disease and stroke) each year. According to the annual EEA reports, Poland is one of the most polluted countries in Europe, experiencing high PMx concentrations during winter that mostly result from large emissions and unfavourable weather conditions in combination with environmental features. Thus, in addition to implementing municipal mitigation strategies, alerting residents to pollution episodes through accurate PMx forecasting is necessary. This research aimed to assess the feasibility of short-term PMx forecasting via machine learning (ML) and the subsequent identification of the primary meteorological covariates. The data comprised 10 years of hourly winter PM10 and PM2.5 concentrations measured at 11 urban air quality monitoring stations, including background, traffic, and industrial sites, in four large Polish agglomerations, viz., Poznań, Kraków, Łódź, and Gdańsk, which cover areas with high population density and diverse environments that extend from the Baltic Sea coast (Tricity) through the lowlands (Poznań and Łódź) to the highlands (Kraków). We tested four ML models: AIC-based stepwise regression, two tree-based algorithms (random forests and XGBoost), and neural networks. Employing analysis and cross-validation, we found that XGBoost performed the best, followed by random forests and neural networks, and stepwise regression performed the worst. This ranking was apparent in the threshold exceedance values of the binary forecasts obtained via regression. Overall, our results confirm the high applicability of ML to short-term air quality prediction with the perfect prog approach.