To investigate the seasonal variation characteristics and sources of PM2.5 chemical components in an economic development zone within a karst plateau city in southwestern China, PM2.5 samples were collected in 2022 around a national air quality monitoring station in the Guiyang Economic and Technological Development Zone. Laboratory analyses and modeling approaches were employed to examine the seasonal variations of carbonaceous components, water-soluble inorganic ions, and trace metal elements in PM2.5, as well as to apportion its sources. The results demonstrate that the average concentrations of OC and EC were 7.0 mu g m(-3) and 3.5 mu g m(-3), accounting for 13.9 % and 7.4 % of PM2.5, respectively. The proportions of SOC in PM2.5 were 5.1 %, 12.0 %, 10.9 %, and 7.9 % across the four seasons. SOC was strongly correlated with OC (R-2 = 0.87) and increased with the enhancement of atmospheric oxidation capacity (Ox level), while OC showed a weak correlation with EC (R-2 = 0.47). The annual average mass concentration of water-soluble ions (WSIIs) was (17.2 +/- 10.0 mu g m(-3)), with SNA accounting for 30.1 % of the water-soluble ions in PM2.5. The overall characteristics showed sulfate > nitrate > ammonium, with an average NO3-/SO42- ratio of 0.4, indicating that stationary sources contributed significantly to atmospheric pollution at this sampling site. Furthermore, ammonium salts mainly existed as (NH4)(2)SO4 and NH4HSO4. The neutralization carriers of nitrate showed seasonal differences: in spring, summer, and autumn, NO3- was significantly positively correlated with NH4+ (r = 0.67-0.81) and mainly present as NH4NO3. It was strongly positively correlated with Ca2+ (r = 0.94) and dominated by metal nitrates such as Ca(NO3)(2) in winter. Among the 14 trace metals, Zn was the dominant element with an average concentration of 53.54 ng m(-3) (accounting for 33.99 %) and the highest concentration in spring (63.50 ng m(-3)). The concentrations of Cr (17.37 ng m(-3)), Mn (16.77 ng m(-3)), and As (12.32 ng m(-3)) all exceeded the relevant standard limits, such as those specified in GB 3095-2012. During the sampling period, the contributions of PM2.5 components were as follows: organic matter (OM, 31.3 %), sulfate (SO42-, 23.9 %), nitrate (NO3-, 9.4 %), ammonium (NH4+, 7.1 %), trace metals (0.3 %), and mineral dust (18.1 %). Positive matrix factorization (PMF) revealed that the sources of PM2.5 were dust (6.0 %), vehicle emissions (13.0 %), secondary nitrate (29.4 %), Industrial sources (10.4 %), biomass burning (22.7 %), and coal combustion source (17.7 %).

Assessing whether residence-based static exposure can represent activity-based exposure is important for refined air pollution exposure assessment, particularly for vulnerable populations such as primary schoolchildren. We quantified static–dynamic exposure differences among primary schoolchildren from 22 primary schools and 138 residential communities and examined their associations with housing price in Luozhuang District, Linyi, China. To support exposure estimation, we developed high-resolution land-use regression (LUR) models integrating dense monitoring observations, road networks, area-of-interest data, and other spatial predictors to generate 100 m × 100 m typical hourly concentrations of PM2.5, PM10, NO2, and O3. After incorporating children’s school-time locations, dynamic exposure estimates remained broadly comparable to static estimates but showed systematic pollutant-specific differences. Significant differences were found for NO2, PM2.5, and O3 in both seasons, with small absolute mean differences of 0.04–0.19 μg/m3. Static estimates tended to overestimate exposure to NO2 and O3 but underestimate exposure to PM2.5. Housing-price-stratified analyses suggested relatively smaller static–dynamic exposure differences in low-price communities and a positive housing-price trend for warm-season O3, although no robust linear association was observed overall. These findings suggest that residence-based exposure estimates can broadly approximate primary schoolchildren’s daily exposure, although ignoring school-time locations may introduce small pollutant-specific deviations in refined exposure assessment.

With waste incineration emerging as the primary waste management approach in China, greenhouse gas (GHG) emissions from leachate treatment have become an increasingly critical environmental concern. This study provides the first comprehensive, field-based assessment of GHG emissions from leachate treatment systems across seven typical municipal solid waste (MSW) incineration plants. The determined emission factors (EFs) were 0.0267 ± 0.0302 kg N2O-N kg-1 TNinf and 0.0002 ± 0.0002 kg CH4 kg-1 CODinf, which are approximately double the IPCC default values for industrial wastewater treatment. In 2022, GHG emissions from incineration leachate treatment reached 1965.2 kt CO2-eq, accounting for 2.2% ± 0.9% of national wastewater treatment emissions. Including leachate from landfills and biological treatment increased the total to 2632.7 ± 1024.8 kt CO2-eq. Uncertainty analysis indicates that inter-facility variability dominates overall uncertainty (77.1%), highlighting the inadequacy of applying a single national EF for accurate accounting. This study identifies leachate treatment as a significant yet underreported source of GHG emissions, underscoring an urgent need to update GHG inventories with leachate-specific EFs and formulate N2O-targeted mitigation strategies.

During the long-range transport of atmospheric mineral dust, anthropogenic pollution can alter the natural dust, yet its impact on the dust's ice-nucleating activity remains unclear. In this study, continuous online measurements of atmospheric ice-nucleating particles (INPs) were conducted in the immersion freezing mode at -20 degrees C, -25 degrees C, and -30 degrees C using an online continuous flow diffusion chamber (CFDC) during the frequent dust period from May 4 to June 20, 2018. Simultaneous comprehensive online observations of particle size distributions and chemical composition were also performed. At -20 degrees C and -25 degrees C, aged dust showed no significant difference in INP concentration versus dust (p > 0.05). In contrast, at -30 degrees C, dust INP concentrations were significantly higher (2.26-2.31 times) than aged dust, with statistically significant differences (p <= 0.01). This result indicated anthropogenic pollution likely alters the dust, leading to a reduction in its ice-nucleating activity at lower temperatures of -30 degrees C. A further analysis of the differences in INP concentrations between aged dust and anthropogenic pollution revealed that under a high PM10 mass loading, there was no significant difference between aged dust and anthropogenic pollution-dominated processes. In contrast, under moderate PM10 mass loading, the INP concentration of aged dust was significantly higher than that of anthropogenic pollution, a difference that may be associated with organic matter content. Based on direct observational evidence, this work clarifies the suppressed effect of anthropogenic pollution on the ice-nucleating activity of East Asian dust, thereby enhancing the understanding of how atmospheric aging processes influence dust ice-nucleating activity.

Characterizing the dynamic evolution of fine particulate matter (PM2.5) across varying pollution persistence is vital for structural air quality management, yet the duration-dependent shifts in driving mechanisms remain poorly understood. This study investigated the complex driving mechanisms of PM2.5 pollution in Linyi, a major industrial and logistics hub in eastern China. We achieved this by integrating multisource data into a positive matrix factorization (PMF)-based machine learning (ML) framework, coupled with SHapley Additive exPlanations (SHAP). Pollution episodes were classified as one-day, two-day, and lasting three or more days. With increasing pollution duration, PM2.5 and its precursors increased stepwise, and the contribution of secondary inorganic aerosols rose from 37.6% on clean days to 47.1% during prolonged episodes. Sulfate formation was driven by humidity-dependent heterogeneous reactions, while nitrate formation was synergistically enhanced under high humidity and elevated atmospheric oxidizing capacity. Six statistically distinct covariance patterns were resolved by PMF and interpreted as potential source categories. SHAP analysis revealed that anthropogenic factors dominated the model predictions across all episodes, with secondary nitrate exhibiting a relative feature importance weight of approximately 38% during polluted periods, reflecting its high predictive sensitivity in explaining PM2.5 fluctuations. Dust inputs became relatively more prominent during two-day episodes, while firework combustion contributed substantially during prolonged episodes. Regional transport patterns shifted with pollution duration: one-day episodes were influenced by southeast and southwest inputs, two-day episodes by northward transport, and prolonged episodes by sustained short-distance transport from the southwest and east, with intermittent long-range contributions. Source region analysis further showed that potential source regions expanded and contribution intensities increased with longer durations. These findings underscore the importance of informing retrospective diagnosis and long-term strategy optimization for pollution episodes of varying durations.

With increasing waste treatment volumes and rising incineration proportions, leachate production from waste incineration inevitably increases. As a high-concentration organic wastewater, its biological treatment generates greenhouse gases (GHGs). To date, few studies have been conducted on the generation rate of leachate and GHG emission characteristics, and GHG emission factors are not provided in the IPCC inventory, hindering both the calculation of total emissions and mitigation research. In this study, a leachate generation rate estimation formula (A = 18.21, B = −2.8, C = −3.7) was established based on measured emission factors and plant data, and then GHG emissions during leachate treatment were investigated. Results show significant spatial and temporal variations in leachate generation rates, with higher rates in summer and lower rates in spring and winter. N2O concentrations are significantly higher than atmospheric background levels, with N2O > CH4 > CO2 in terms of concentration differences. N2O and CH4 emissions range from 2.93 g t−1 to 1146.56 g t−1 and 0.93 g t−1 to 137.83 g t−1, with median values of 74.62 ± 12.78 g t−1 and 8.61 ± 0.49 g t−1, respectively. Aeration adjustment significantly affects N2O emissions. CO2 emissions from aeration account for 9.51% of N2O and CH4 emissions. Positive correlations were observed between N2O emissions and both COD (chemical oxygen demand) and TN (total nitrogen) removal efficiencies, whereas a negative correlation existed with DO (dissolved oxygen) content. Similarly, CH4 emissions exhibited positive associations with COD and TC (total carbon), while CO2 emissions demonstrated positive correlations with TP (total phosphorus).

As a key precursor of hydroxyl (OH) radicals, the budget of nitrous acid (HONO) at different altitudes has received extensive attention. In this study, vertically resolved observations of HONO, NO2, O3, and HCHO were conducted during an autumn field campaign in Beijing in 2019. The significant correlation between HONO and NO2, along with variations in their ratios across different altitudes, underscores the importance of aerosol surface chemistry in HONO formation and its altitude-dependent behavior. To enhance the model performance, the heterogeneous conversion of NO2 and its photochemical enhancement are incorporated into the 1D model. The simulations reveal that the nocturnal HONO production is dominated by the heterogeneous conversion of NO2 both at the surface and aloft. During the daytime, ground-surface sources of HONO are mainly driven by nitrate photolysis and light-enhanced heterogeneous conversion of NO2. Meanwhile, a large portion of the HONO generated at the surface is transported upwards through vertical mixing. In the higher atmosphere, HONO originates from vertical transport and in situ processes. As precursors of OH radicals, the observed concentrations of HONO, O3, and HCHO exhibit distinct diurnal variations and vertical distribution patterns. HONO contributes to OH radical production predominantly during the early morning across all layers and it even becomes the main contributor throughout the daytime in the lowest layer near the ground, while O3 and HCHO become more prominent towards midday, especially in the higher layers. These results will be beneficial for a deeper understanding of the atmospheric oxidation process within the urban boundary layer.

The spatial representativeness (SR) of air quality monitoring sites is critical for ensuring that gathered data accurately reflect the broader area's air quality. Evaluating the SR of sites at a national scale and its long‐term trends is particularly important for countries like China, where both air quality and monitoring networks have changed dramatically over time. Here, we used 1‐km daily air pollutant concentrations from the China High Air Pollutants dataset to assess the yearly SR of state‐controlled sites in China from 2013 to 2022 for multiple pollutants. With the number of sites increasing from 460 in 2013 to 1,590 in 2022, our results showed that the total SR area of sites increased by 89% for PM 2.5 , 149% for PM 10 , and 2,190% for O 3 . While the number of sites mainly drove these increases, its impact varied at different phases. Interestingly, the rise in sites from 2020 to 2022 actually led to a decrease in the total SR area for PM 2.5 (−18,300 km 2 ) and PM 10 (−14,200 km 2 ). Additionally, we found that applying SR to pollution exposure assessments did not improve their accuracy at national and city levels when compared to the official method, which involves exposure calculation using arithmetic mean aggregation of monitoring sites. This was related to poor SR performance, with more than half of the population being uncovered by SR areas in more than 85% of Chinese cities. Nevertheless, we demonstrated the benefits of applying SR for city‐level air quality attainment.

Daily thresholds of meteorological factors relative to severe summer ozone pollution are determined in the North China Plain (NCP), the Fenhe River and Weihe River Plain (FWP), the Sichuan Basin (SCB), the Changjiang River Plain (CJR) and Pearl River Delta (PRD) by combing ozone concentrations at air quality monitoring stations and meteorological elements at weather stations. These regions share same daily thresholds, namely maximum temperature above 30 °C, relative humidity below 80%, rainfall below 10 mm and radiation in the scope of 17~27 MJ/m2 together with wind speed in the range of 0.5~3.0 m/s. The adverse meteorological frequency combining daily thresholds of wind speed and radiation shows individual trend and periodic characteristics in each region after conducting 10-year moving average, namely rising with 3~6 percentage points/decade in NCP (in June), FWP (in June, July and August) and CJR (in July) while decreasing with 2~3 percentage points in SCB in July and August. However, there are no apparent trends in PRD. Additionally, these frequencies are periodic with 8.3 years to 25 years. The frequencies are positively related to Western Pacific Subtropical High (WPSH) in NCP, FWP, CJR and PRD, while negatively relevant in SCB. The correlate coefficients between Southern Oscillation and the frequencies vary in regions and months. With cyclo-stationary empirical orthogonal function analysis, we also substantiate impacts of global warming, Pacific Decadal Oscillation, El-Nino and La-Nina on WPSH in two typical months. These would give us more insights on meteorological effects on ozone pollution and be helpful for its projection.

The trends and characteristics of global CH4 emissions were analyzed using greenhouse gas data reported by both Annex I and non-Annex I countries under the United Nations Framework Convention on Climate Change(UNFCCC)from 1990 to 2021.The results show the following:(1)In 2021,the cumulative CH4 emissions from the 42 nations listed in Annex I of the UNFCCC amounted to 1871521.79 kt CO2 eq.The top 10 countries account for 82.0％of the total CH4 emissions.(2)Most Annex I countries showed a gradual decline in CH4 emissions over the period.In contrast,emissions from non-Annex I countries have increased year by year.Notably,CH4 emissions in the United States,the European Union,the Russian Federation,and Ukraine decreased by 14.0％,37.4％,24.0％,and 60.9％,respectively.(3)In 2020,the CH4 emissions of the agriculture,energy,waste treatment and LULUCF(land use,land-use change and forestry)sectors in Annex I countries were 72240.43,63863.51,41573.08,and 889019 million tons of CO2 eq,accounting for 38.6％,34.1％,22.2％,and 4.8％,respectively.Among non-Annex I countries,the main CH4 sources vary by country.In China and Mexico,energy and agriculture were the largest contributors,accounting for 44.8％and 40.2％in China,and 34.4％and 43.3％in Mexico,respectively.In India,Brazil,Nigeria,Argentina,and Vietnam,agriculture dominated,contributing 73.8％,75.8％,59.7％,60.3％,and 58.5％of total emissions,respectively.Indonesia was an exception,with waste treatment being the primary source,accounting for 64.8％of its total CH4 emissions.

Despite significant progress in air quality improvement, heavy fine particulate matter (PM2.5) pollution events persist in China. The pollution characteristics of PM2.5 vary during different pollution levels, highlighting the necessity for a deeper understanding of its underlying driving factors and regional transport. This study systematically investigated the compositional characteristics, drivers, and regional transport of PM2.5 in Linyi by integrating multi-data fusion analysis, positive matrix factorization-machine learning-shapley additive explanation (PMF-ML-SHAP), and concentration weighted trajectory model. The results revealed that heterogeneous reactions dominated SO42 - formation during polluted periods, while homogeneous reactions drove NO3- formation. In constructing the integrated PMF-ML-SHAP framework, RandomizedSearchCV and KFold techniques significantly enhanced CatBoost model performance. The contribution of local sources increased progressively from 85.6 % to 91.4 % with rising PM2.5 levels, with secondary nitrate formation emerging as the dominant driver of PM2.5 pollution. The influence share of biomass combustion was higher during clean (CP, 20.4 %) and slightly polluted periods (SPP, 17.3 %), while that of firework combustion was higher during heavily polluted periods (HPP, 25.9 %). Among meteorological factors, wind speed and ultraviolet radiation intensity played critical roles in PM2.5 dispersion and secondary aerosol formation. Regional transport analysis indicated that short-range and medium-range transport air masses primarily influenced CP and SPP, whereas local transport air masses dominated during moderately polluted periods (MPP, 54.4 %) and HPP (58.2 %). This study provides new insights into the drivers of PM2.5 pollution during different pollution levels, offering a scientific basis for targeted pollution control strategies in Linyi and similar industrial cities.

Meteorological factors have profound effects on the severe PM2.5 pollution in winter. The thresholds of wind speed, humidity, rainfall, and snowfall related to it are individually identified in six regions of China by combining meteorological stations' and air quality stations' data. Five mm is the daily threshold for precipitation, not only for rainfall but also for snowfall because more than 80% of severe PM2.5 pollution cases occurred below this magnitude. However, in the no-precipitation condition the thresholds comprising wind speed and relative humidity varied in regions, indicating wind speed of 2 m s-1 and relative humidity of 60% not quite practical in China. Furthermore, in this condition the frequency of adverse meteorological conditions associating with thresholds of wind speed and relative humidity evolved increasingly with 3-5 percentage points per decade and periodically with 7-16.7 years after taking 11 -year moving average in the Fenhe River and Weihe River Plain (FWP), the Northeast China Plain (NEC) and the North China Plain (NCP) from 1960 to 2019. Whereas in the Sichuan Basin similar frequency peaked in the 1990s then decreased till the 2010s. In the NCP, FWP, and the Changjiang River Plain, such frequencies' variation could be attributed to the East Asian Trough whose intensity is defined as the geopotential height gradient at 500 hPa. In addition, East Asian Trough is periodic with 7.7-20 years after taking 11 -year moving average. It would be helpful in planning air quality improvements if these results are taken into consideration.

To maintain air quality and mitigate pollution during the 24th Olympic Winter Games, strict control measures were implemented in Beijing and surrounding areas, including Linyi. Fine particulate matter (PM2.5) sampling was conducted in the urban area of Linyi between November 10th, 2021, and February 20th, 2022. This sampling period was divided into a no-control period (NCP) and a control period (CP). The PM2.5 concentration decreased by 25.0% during the CP, whereas the atmospheric oxidation increased. This led to the formation of secondary components of PM2.5. Although the conversion rate of the precursors into secondary components was increased during the CP, the nitrate (NO3−) and ammonium (NH4+) concentrations were reduced due to emission reduction. Elemental carbon (EC) and primary organic carbon concentrations were also decreased, whereas the organic carbon (OC)/EC ratio and proportion of secondary organic carbon in PM2.5 increased. Total detected inorganic element concentrations decreased by 23.6% during the CP. The results of source apportionment revealed that secondary sources were the leading PM2.5 contributors during both periods. Vehicle pollution prevention and control should be strengthened as no restrictions on private-car travel during the CP. Controlling combustion and industrial sources had a significant impact on air quality during the CP. Biomass burning and industrial emissions declined from 20.7% to 16.1% (NCP) to 2.0% and 9.1% (CP), respectively. Backward trajectories revealed that air masses largely originated from local regions during both periods, with the highest PM2.5 concentrations found in air masses from the north (NCP) and east (CP) of Linyi. Potential source regions were also identified. The location of these regions showed that local and regional collaborative control of PM2.5 is urgently needed, particularly in northern and southern Linyi (NCP) and eastern Linyi (CP).

Situated in close proximity to the Korea Peninsula and the Japan Islands, Shandong Province in China (SDP) has emerged as a focal point for global attention due to its significant surface ozone (O3) and aerosol pollution. Despite this attention, there are notable gaps in knowledge across various interconnected research domains, encompassing climate change, atmospheric circulation, anthropogenic emissions, and the chemistry of O3 and aerosols. The impact of frequent heat waves on regional O3 and aerosol pollution remains unclear, while our comprehension of atmospheric circulation dynamics and the chemistry involved with O3 and aerosols is still hampered by substantial limitations. The unique topography and geographical setting of SDP, with its complex interplay of factors like sea-land breezes, mountain-valley winds, and urban heat islands, make it an ideal location for investigating the dynamics of O3 and aerosol chemistry. Moreover, it is essential to explore the effects of transboundary transport on O3 and aerosol pollution and delve into the underlying mechanisms contributing to their combined pollution effects. To bridge these knowledge gaps, the Innovative Collaboration-Based Ozone and Aerosol Observation Network in Northeast Asia has been established through collaborative efforts involving the Bureaus of Ecology and Environment, Meteorology in SDP, and the Chinese Academy of Sciences. This network encompasses various components, including air quality and weather stations, a network of low-cost air quality sensors, monitoring stations focused on atmospheric photochemical smog and aerosol chemical speciation, and vertical measurement systems targeting O3 and its precursor gases (utilizing light detection and ranging, balloon sounding, and drone-based measurements), as well as measurements conducted via vehicles and ships. Additionally, preliminary findings from this comprehensive observational campaign will be shared. SIGNIFICANCE STATEMENT: This research aims to understand how climate change, atmospheric circulation, and pollution from ozone and aerosols interact in northeast Asia, particularly in Shandong Province. By establishing a comprehensive observation network, we seek to uncover the dynamics of air pollution in diverse environments. Our work provides crucial data to improve air quality and inform climate change mitigation strategies, emphasizing the importance of interdisciplinary collaboration and sustained monitoring efforts to address environmental challenges. This initiative not only enhances scientific understanding but also fosters regional cooperation and contributes significantly to global atmospheric science.

Most health studies have used residential addresses to assess personal exposure to air pollution. These exposure assessments may suffer from bias due to not considering individual movement. Here, we collected 45,600 hourly movement trajectory data points for 185 individuals in Nanjing from COVID-19 epidemiological surveys. We developed a fusion algorithm to produce hourly 1-km PM2.5 concentrations, with a good performance for out-of-station cross validation (correlation coefficient of 0.89, root-mean-square error of 5.60 mu g/m(3), and mean absolute error (MAE) of 4.04 mu g/m(3)). Based on these PM2.5 concentrations and location data, PM2.5 exposures considering individual movement were calculated and further compared with residence-based exposures. Our results showed that daily residence-based exposures had an MAE of 0.19 mu g/m(3) and were underestimated by <1% overall. For hourly residence-based exposures, the MAE exhibited a diurnal variation: it decreased from 0.58 mu g/m(3) at 09:00 to 0.44 mu g/m(3) at 12:00 and then continuously increased to 0.74 mu g/m(3) at 17:00. The biases also depended on activity types and distances from home to activity locations. Specifically, the largest MAE (3.86 mu g/m(3)) occurred in visits that were among the top four types of activity other than being at home. As distances changed from <10 to >30km, the degree of underestimation for hourly residence-based exposures increased from 1% to 6%. This trend was more obvious for work activities, suggesting that personal exposure assessments should consider individual movement for work cases with long commuting distances. (c) 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)

Elucidating the chemical composition, sources, and health risks of fine particulate matter (PM2.5) is crucial for effectively preventing and controlling air pollution. This study collected PM2.5 samples in Linyi from November 10, 2021, to October 15, 2022, spanning the period of the 2022 Winter Olympics and Paralympics. The analysis focused on seasonal variations in the chemical composition of PM2.5, including water-soluble ions, inorganic elements, and carbonaceous aerosols. Results from the random forest model indicated that control measures during the Olympics and Paralympics reduced PM2.5 concentrations by 21.5% in Linyi. Organic matter was the dominant component of PM2.5, followed by NO3- , SO42- , and NH4+. Among secondary inorganic ions, SO4 2exhibited the highest concentration in summer, while NO3- and NH4+ showed the lowest concentrations. The inorganic elements S, K, Fe, and Si had high mean annual concentrations, underscoring the need for targeted control measures for plate production, bulk coal burning, and biomass combustion in Linyi. The organic carbon (OC) to elemental carbon ratio (17.7-20.5) in Linyi was high, highlighting the importance of addressing secondary OC pollution. According to the positive matrix factorization model, coal burning, and the secondary formation processes of sulfate and nitrate were the dominant sources of PM2.5. Backward air mass trajectories revealed substantial contributions from the southeastern, local, and southwestern regions of Linyi. This suggests the need for enhanced regional joint prevention and control efforts between Linyi and neighboring cities, such as Rizhao and Jining in Shandong Province, as well as northern cities in Jiangsu Province. The highest noncarcinogenic and carcinogenic risks (CRs) were associated with As. coal burning posed significant noncarcinogenic risks and a moderate CR, contributing 41.7% and 44.0% of the total health risk, respectively. These findings are crucial for developing effective air pollution prevention and control strategies.

To comprehensively investigate the seasonal variations in submicron particles (PM 1 = NR-PM 1 + BC) composition, sources and chemical processes, an Aerodyne high -resolution time -of -flight aerosol mass spectrometer (HR-ToF-AMS), along with a multiangle absorption photometer (MAAP), was deployed to observe at urban sites in the North China Plain from October 1, 2015, to July 31, 2016. The PM 1 average mass were 36.4 +/- 39.0, 32.9 +/- 20.7, 40.4 +/- 52.4 and 99.0 +/- 88.7 mu g m - 3 in spring, summer, autumn and winter, respectively. On the whole, the OA accounted for the largest component of PM 1 during the four seasons, with the highest contribution in winter, accounting for 49% on average, followed by autumn (47%), spring (40%) and summer (31%). The secondary inorganic aerosol (SIA) mass concentration in summer was the lowest but contributed the most to PM 1 in summer (62%), and it was higher than that in spring (46%), autumn (43%) and winter (41%). Positive matrix factorization (PMF) was used to differentiate the high -resolution mass spectra (HRMS) of organic aerosol (OA) into six factors, including two secondary OA (highly oxidized, low -volatility oxygenated organic aerosols (LVOOA) and less oxidized, semi -volatile oxygenated OA (SV-OOA)) and four primary OA (including cooking -related OA (COA), hydrocarbon -like OA (HOA), biomass burning OA (BBOA) and coal combustion OA (CCOA)). OOA (LV-OOA + SV-OOA) exhibited the highest OA loading in summer (59%), while POA (CCOA + COA + HOA) exhibited the highest OA loading in winter (62%). The OA exhibited similar bimodal diurnal patterns during the four seasons, the nitrate and sulfate seasonal diurnal variations slightly differed, and the peak value occurred at night in winter and in the morning during the other seasons. The trend of the LV-OOA and SV-OOA diurnal variation in spring, autumn and winter appeared small difference, and the summer vary differently compared to other seasons. However, the diurnal mass concentration in winter was much higher than other seasons. The COA diurnal pattern peaked at noon and at night during the four seasons, corresponding to the peak hours of dining activities. The HOA, BBOA and CCOA seasonal diurnal patterns showed low concentrations during the daytime and high concentrations at night. OA on HPD was 6, 7 and 7 times higher than that on CD in spring, autumn and winter. The SIA concentration significantly increased on HPD in spring (69.3 mu g m - 3 , 52%), autumn (80.6 mu g m - 3 , 50%) and winter (82.4 mu g m - 3 , 43%). The organic nitrate average mass concentrations were 0.6 +/- 0.5, 0.7 +/- 0.2, 0.8 +/- 1.2 and 2.5 +/- 2.3 mu g m - 3 in spring, summer, autumn and winter, respectively. The organic nitrate diurnal pattern appeared at night was higher than that during the daytime in spring, autumn and winter. The opposite diurnal pattern emerged in summer. The average mass concentration of polycyclic aromatic hydrocarbons (PAHs) during the entire observation period reached 73.2 +/- 154.2 ng m - 3 , and the seasonal average mass were 30.4 +/- 30.6, 10.7 +/- 4.0, 40.5 +/- 10.3 and 220.3 +/- 246.7 ng m - 3 in spring, summer, autumn and winter, respectively. The highest diurnal values in spring, summer, autumn and winter were 44.2, 11.7, 71.6 and 366.8 ng m -3 , respectively.

To comprehensively investigate the seasonal variations in the submicron particle composition, sources and chemical processes, an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS), along with a multiangle absorption photometer (MAAP) and other auxiliary instruments, was deployed to acquire the temporal variations in the mass loading and chemical composition of submicron particles (PM1, particulate matter with a vacuum aerodynamic diameter ≤1 μm) at urban sites in the North China Plain from October 1, 2015, to July 31, 2016. The average mass concentrations of PM10 and PM2.5 were 116.5±108.7 and 47.5±51.1 μg m-3, 72.0±42.6 and 45.5±28.3 μg m-3, 79.5±89.5 and 52.3±66.6 μg m-3 and 194.2±173.4 and 131.2±121.3 μg m-3, respectively, in spring, summer, autumn and winter, respectively. The PM1 average mass concentrations, including non-refractory submicron particles (NR-PM1) measured by the HR-ToF-AMS and black carbon (BC) measured by the MAAP in spring, summer, autumn and winter, were 36.4±39.0, 32.9 ± 20.7, 40.4±52.4 and 99.0±88.7 μg m-3, respectively, in this study. Organics dominated PM1 in spring, summer, autumn and winter, with average mass concentrations of 14.5±14.8, 10.4±5.1, 19.1±23.7 and 48.8±44.2 μg m-3, respectively, accounting for 44%, 41%, 50% and 52%, respectively, of PM1. The secondary inorganic aerosol (SIA) mass concentration in summer was the lowest but contributed the most to PM1 in summer (50%), and it was higher than that in spring (44%), autumn (38%) and winter (38%). Positive matrix factorization (PMF) was used to differentiate the high-resolution mass spectra (HRMS) of organic aerosol (OA) into six factors, including two secondary OA (highly oxidized, low-volatility oxygenated organic aerosols (LV-OOA) and less oxidized, semivolatile oxygenated OA (SV-OOA)) and four primary OA (including cooking-related OA (COA), hydrocarbon-like OA (HOA), biomass burning OA (BBOA) and coal combustion OA (CCOA)). OOA (LV-OOA+SV-OOA) exhibited the highest OA loading in summer (59%), while POA (CCOA+COA+HOA) exhibited the highest OA loading in winter (62%). The OA exhibited similar bimodal diurnal patterns during the four seasons, the nitrate and sulfate seasonal diurnal variations slightly differed, and the peak value occurred at night in winter and in the morning during the other seasons. The OOA diurnal variation slowly increased during the daytime due to the influence of photochemical reactions. The COA diurnal pattern peaked at noon and at night during the four seasons, corresponding to the peak hours of dining activities. The HOA, BBOA and CCOA seasonal diurnal patterns showed low concentrations during the daytime and high concentrations at night. OA constituted the predominant component of PM1 on clean days (CD), light to moderate polluted days (LMPD) and heavy polluted days (HPD) during the four seasons, and the OA mass on HPD in spring, autumn and winter was 6, 7 and 7 times higher than that on CD. The SIA concentration significantly increased on HPD in spring (69.3 μg m-3, 52%), autumn (80.6 μg m-3, 50%) and winter (82.4 μg m-3, 43%) relative to CD. The inorganic and organic nitrate average mass concentrations were 4.1±5.7 and 0.6±0.5 μg m-3, 6.1±5.8 and 0.7±0.2 μg m-3, 6.8±11.0 and 0.8±1.2 μg m-3, and 7.6±8.2 and 2.5±2.3 μg m-3, respectively, in spring, summer, autumn and winter, respectively. The organic nitrate diurnal pattern remained relatively stable, and the mass concentration at night was high

Ambient PM2.5 pollution is a leading environmental health risk factor worldwide. The spatial resolution of PM2.5 concentrations and population strongly impacts PM2.5-related health impact estimates. However, long-term variations and regional differences in this impact have rarely been explored, particularly in China. Here, by aggregating satellite-derived PM2.5 concentration and population datasets at 1-km resolution in China to coarser resolutions (10, 50, and 100 km), we evaluated decadal changes in the impact of resolution on health assessments at national and local scales. For the sensitivity of population-weighted mean (PWM) PM2.5 concentrations to resolution, we found that the national PWM PM2.5 concentration decreased with coarser resolutions; this pattern was widely observed and was more obvious in southern and central China and the Sichuan Basin. The results showed that the sensitivity of national PWM PM2.5 concentrations to resolution continuously weakened from 2010 to 2020, likely due to a reduction in the spatial heterogeneity of PM2.5 concentrations in regions with high sensitivity. This weakness caused a large underestimation of the long-term trend of national PWM PM2.5 using a 100-km resolution, which was 7% lower than the trend at 1 km. Regarding the sensitivity of PM2.5attributable mortality to resolution, most of China exhibited a pattern in which attributable mortality decreased with coarser resolution. The sensitivity of the estimated PM2.5-attributable mortality to resolution also weakened over time on a national scale and in most parts of China. Nevertheless, the weakness for mortality sensitivity was not as apparent as for PWM PM2.5 sensitivity. This was likely because different drivers played distinct roles in the temporal variation of the mortality sensitivity: population aging enhanced the sensitivity, and variations in PM2.5 concentrations and population distribution both weakened the sensitivity. However, the national attributable mortality trend at a 100-km resolution was still underestimated by 1.75% relative to the 1-km resolution.

Previous field observations have shown that NO3 and related species develop distinct vertical profiles in urban areas. In this study, we utilized the Platform for Atmospheric Chemistry and Vertical Transport (PACT) under observation constrains to simulate NO3 and related substances during field campaign in Beijing in the autumn of 2019. The simulations of NO3 for each layer have achieved a good agreement (R approximate to 0.92) with the LP-DOAS observations for the whole period. The vertical NO3 budget analysis concluded that direct loss dominates at the lowest layer (over 70%), while indirect loss is most significant at the upper layer. But what we found in this study is that direct NO3 loss is not only confined to the ground, but also has a vital proportion of 50% at high altitudes. What's more, case study of fast NO3 decay situation (Case II) shows that sometimes direct loss in the highest layer can account for more than 90% of the total NO3 loss, while indirect loss is more significant in the lowest layer than in the highest layer. In contrast, the poor NO3 simulation (Case III) indicates that the NO3 loss in the highest layer was all occupied by indirect loss. Loss pathway of NO3 in the high altitudes can be completely occupied by direct loss or indirect loss respectively, showing the complexity of the NO3 loss path. The sensitivity test on indirect NO3 loss rates via N2O5 heterogeneous reaction at highest layer suggested the importance of chemical equilibrium between NO3 and N2O5 on the budgets.