Humidity and PM2.5 composition determine atmospheric light extinction in the arid region of northwest China

Atmospheric visibility can directly reflect the air quality. In this study, we measured water-soluble ions (WSIs), organic and element carbon (OC and EC) in PM_2.5 from September 2017 to August 2018 in Urumqi, NW China. The results show that SO₄^2?, NO₃^? and NH₄⁺ were the major WSIs, together accounting for 7.32%–84.12% of PM_2.5 mass. Total carbon (TC=OC+EC) accounted for 12.12% of PM_2.5 mass on average. And OC/EC > 2 indicated the formation of secondary organic carbon (SOC). The levels of SO₄^2?, NO₃^? and NH₄⁺ in low visibility days were much higher than those in high visibility days. Relative humidity (RH) played a key role in affecting visibility. The extinction coefficient (b_ext) that estimated via Koschmieder formula with visibility was the highest in winter (1441.05 ± 739.95 Mm^?1), and the lowest in summer (128.58 ± 58.00 Mm^?1). The b_ext that estimated via IMPROVE formula with PM_2.5 chemical component was mainly contributed by (NH₄)₂SO₄ and NH₄NO₃. The b_ext values calculated by both approaches presented a good correlation with each other (R² = 0.87). Multiple linear regression (MLR) method was further employed to reconstruct the empirical regression model of visibility as a function of PM_2.5 chemical components, NO₂ and RH. The results of source apportionment by Positive Matrix Factorization (PMF) model showed that residential coal combustion and vehicle emissions were the major sources of b_ext.     

Fine particulate matter (PM2.5/PM1.0) in Beijing, China: Variations and chemical compositions as well as sources

Particulate matter (i.e., PM_1.0 and PM_2.5), considered as the key atmospheric pollutants, exerts negative effects on visibility, global climate, and human health by associated chemical compositions. However, our understanding of PM and its chemical compositions in Beijing under the current atmospheric environment is still not complete after witnessing marked alleviation during 2013–2017. Continuous measurements can be crucial for further air quality improvement by better characterizing PM pollution and chemical compositions in Beijing. Here, we conducted simultaneous measurements on PM in Beijing during 2018–2019. Results indicate that annual mean PM_1.0 and PM_2.5 concentrations were 35.49 ± 18.61 µg/m³ and 66.58 ± 60.17 µg/m³, showing a positive response to emission controls. The contribution of sulfate, nitrate, and ammonium (SNA) played an enhanced role with elevated PM loading and acted as the main contributors to pollution episodes. Discrepancies observed among chemical species between PM_1.0 and PM_2.5 in spring suggest that sand particles trend to accumulate in the range of 1–2.5 µm. Pollution episodes occurred accompanied with southerly clusters and high formation of SNA by heterogeneous reactions in summer and winter, respectively. Results from positive matrix factorization (PMF) combined with potential source contribution function (PSCF) models showed that potential areas were seasonal dependent, secondary and vehicular sources became much more important compared with previous studies in Beijing. Our study presented a continuous investigation on PM and sources origins in Beijing, which provides a better understanding for further emission control as well as a reference for other cities in developing countries.     

Cause of PM2.5 pollution during the 2016-2017 heating season in Beijing, Tianjin, and Langfang, China

To investigate the cause of fine particulate matter(particles with an aerodynamic diameter less than 2.5 um,PM2.5) pollution in the heating season in the North China Plain(specifically Beijing,Tianjin,and Langfang),water-soluble ions and carbonaceous components in PM_2.5were simultaneously measured by online instruments with 1-hr resolution,from November 15,2016 to March 15,2017.The results showed extreme severity of PM_2.5 pollution on a regional scale.Secondary inorganic io...     

Chemical characterization and source identification of PM2.5 in Luoyang after the clean air actions

Luoyang is a typical heavy industrial city in China, with a coal-dominated energy structure and serious air pollution. Following the implementation of the clean air actions, the physicochemical characteristics and sources of PM_2.5 have changed. A comprehensive study of PM_2.5 was conducted from October 16, 2019 to January 23, 2020 to evaluate the effectiveness of previous control measures and further to provide theory basis for more effective policies in the future. Results showed that the aerosol pollution in Luoyang in autumn and winter is still serious with the average concentration of 91.1 μg/m³, although a large reduction (46.9%) since 2014. With the contribution of nitrate increased from 12.5% to 25.1% and sulfate decreased from 16.7% to 11.2%, aerosol pollution has changed from sulfate-dominate to nitrate-dominate. High NO₃⁻/SO₄²⁻ ratio and the increasing of NO₃⁻/SO₄²⁻ ratio with the aggravation of pollution indicating vehicle exhaust playing an increasingly important role in PM_2.5 pollution in Luoyang, especially in the haze processes. Secondary inorganic ions contributed significantly to the enhancement of PM_2.5 during the pollution period. The high value of Cl⁻/Na⁺ and EC concentration indicate coal combustion in Luoyang is still serious. The top three contributor sources were secondary inorganic aerosols (33.3%), coal combustion (13.6%), and industrial emissions (13.4%). Close-range transport from the western and northeastern directions were more important factors in air pollution in Luoyang during the sampling period. It is necessary to strengthen the control of coal combustion and reduce vehicle emissions in future policies.     

PM2.5 chemical composition and health risks by inhalation near a chemical complex

Particulate matter (PM_2.5) samples were collected in the vicinity of an industrial chemical pole and analysed for organic and elemental carbon (OC and EC), 47 trace elements and around 150 organic constituents. On average, OC and EC accounted for 25.2% and 11.4% of the PM_2.5 mass, respectively. Organic compounds comprised polycyclic aromatic hydrocarbons (PAHs), alkylated PAHs, anhydrosugars, phenolics, aromatic ketones, glycerol derivatives, aliphatic alcohols, sterols, and carboxyl groups, including aromatic, carboxylic and dicarboxylic acids. Enrichment factors > 100 were obtained for Pb, Cd, Zn, Cu, Sn, B, Se, Bi, Sb and Mo, showing the contribution of industrial emissions and nearby major roads. Principal component analysis revealed that vehicle, industrial and biomass burning emissions accounted for 66%, 11% and 9%, respectively, of the total PM_2.5-bound PAHs. Some of the detected organic constituents are likely associated with plasticiser ingredients and thermal stabilisers used in the manufacture of PVC and other plastics in the industrial complex. Photooxidation products of both anthropogenic (e.g., toluene) and biogenic (e.g., isoprene and pinenes) precursors were also observed. It was estimated that biomass burning accounted for 13.8% of the PM_2.5 concentrations and that secondary OC represented 37.6% of the total OC. The lifetime cancer risk from inhalation exposure to PM_2.5-bound PAHs was found to be negligible, but it exceeded the threshold of 10⁻⁶ for metal(loi)s, mainly due to Cr and As.     

Characteristics of PM2.5 pollution in Beijing after the improvement of air quality

Following the implementation of the strictest clean air policies to date in Beijing, the physicochemical characteristics and sources of PM_2.5 have changed over the past few years. To improve pollution reduction policies and subsequent air quality further, it is necessary to explore the changes in PM_2.5 over time. In this study, over one year (2017–2018) field study based on filter sampling (TH-150C; Wuhan Tianhong, China) was conducted in Fengtai District, Beijing, revealed that the annual average PM_2.5 concentration (64.8 ± 43.1 μg/m³) was significantly lower than in previous years and the highest PM_2.5 concentration occurred in spring (84.4 ± 59.9 μg/m³). Secondary nitrate was the largest source and accounted for 25.7% of the measured PM_2.5. Vehicular emission, the second largest source (17.6%), deserves more attention when considering the increase in the number of motor vehicles and its contribution to gaseous pollutants. In addition, the contribution from coal combustion to PM_2.5 decreased significantly. During weekends, the contribution from EC and NO₃^? increased whereas the contributions from SO₄^2?, OM, and trace elements decreased, compared with weekdays. During the period of residential heating, PM_2.5 mass decreased by 23.1%, compared with non-heating period, while the contributions from coal combustion and vehicular emission, and related species increased. With the aggravation of pollution, the contribution of vehicular emission and secondary sulfate increased and then decreased, while the contribution of NO₃^? and secondary nitrate continued to increase, and accounted for 34.0% and 57.5% of the PM_2.5 during the heavily polluted days, respectively.     

Concentrations and chemical compositions of PM10 during hazy and non-hazy days in Beijing

In order to study the concentrations of major components,characteristics and comparison in hazy and non-hazy days of PM₁₀ in Beijing,aerosol samples were collected at urban site in Beijing from December 29,2014 to January 22,2015.Heavy metals like Zn,Pb,Mn,Cu,As,V,Cr and Cd were deeply studied considering their toxic effects on human being;nine water-soluble inorganic ions(SO₄^2-,NO₃^-,NH₄⁺,Na⁺,K⁺,Cl...     

PM2.5 concentration estimation using convolutional neural network and gradient boosting machine

Surface monitoring, vertical atmospheric column observation, and simulation using chemical transportation models are three dominant approaches for perception of fine particles with diameters less than 2.5 micrometers (PM_2.5) concentration. Here we explored an image-based methodology with a deep learning approach and machine learning approach to extend the ability on PM_2.5 perception. Using 6976 images combined with daily weather conditions and hourly time data in Shanghai (2016), trained by hourly surface monitoring concentrations, an end-to-end model consisting of convolutional neural network and gradient boosting machine (GBM) was constructed. The mean absolute error, the root-mean-square error and the R-squared for PM_2.5 concentration estimation using our proposed method is 3.56, 10.02, and 0.85 respectively. The transferability analysis showed that networks trained in Shanghai, fine-tuned with only 10% of images in other locations, achieved performances similar to ones from trained on data from target locations themselves. The sensitivity of different regions in the image to PM_2.5 concentration was also quantified through the analysis of feature importance in GBM. All the required inputs in this study are commonly available, which greatly improved the accessibility of PM_2.5 concentration for placed and period with no surface observation. And this study makes an exploratory attempt on pollution monitoring using graph theory and deep learning approach.     

沙尘暴PM2.5水溶和有机成分对巨噬细胞的损伤

用超纯水或二氯甲烷从甘肃省武威市和内蒙古包头市采集的沙尘暴细颗粒物(PM2.5)中提取水溶成分和有机成分,于体外处理大鼠肺泡巨噬细胞4h,测定细胞谷胱甘肽(GSH)和丙二醛(MDA)含量、质膜ATP酶活性、膜表层和膜脂疏水区流动性、胞质内游离钙离子(Ca2+)浓度以及细胞培养液中乳酸脱氢酶(LDH)和酸性磷酸酶(ACP)活性.结果表明,沙尘暴PM2.5水溶成分可抑制质膜Ca2+-Mg2+-ATP酶、Na+-K+-ATP酶活性,降低质膜表层和膜脂疏水区流动性,增加胞质LDH外渗,并使细胞脂质过氧化作用增强、抗氧化能力减弱,但对ACP和Ca2+浓度影响不大;有机成分除引起胞质LDH渗漏、质膜Na+-K+-ATP酶活性下降外,对其它测定指标的影响无统计学意义.说明沙尘暴PM2.5水溶和有机成分均可对肺泡巨噬细胞产生毒性,其中水溶成分的毒性作用大于有机成分.     

Seasonal differences in sources and formation processes of PM2.5 nitrate in an urban environment of North China

Nitrate (NO₃⁻) has been the dominant ion of secondary inorganic aerosols (SIAs) in PM_2.5 in North China. Tracking the formation mechanisms and sources of particulate nitrate are vital to mitigate air pollution. In this study, PM_2.5 samples in winter (January 2020) and in summer (June 2020) were collected in Jiaozuo, China, and water-soluble ions and (δ¹⁵N, δ¹⁸O)-NO₃⁻ were analyzed. The results showed that the increase of NO₃⁻ concentrations was the most remarkable with increasing PM_2.5 pollution level. δ¹⁸O-NO₃⁻ values for winter samples (82.7‰ to 103.9‰) were close to calculated δ¹⁸O-HNO₃ (103‰ ± 0.8‰) values by N₂O₅ pathway, while δ¹⁸O-NO₃⁻ values (67.8‰ to 85.7‰) for summer samples were close to calculated δ¹⁸O-HNO₃ values (61‰ ± 0.8‰) by OH oxidation pathway, suggesting that PM_2.5 nitrate is largely from N₂O₅ pathway in winter, while is largely from OH pathway in summer. Averaged fractional contributions of P_N2O5+H2O were 70% and 39% in winter and summer sampling periods, respectively, those of P_OH were 30% and 61%, respectively. Higher δ¹⁵N-NO₃⁻ values for winter samples (3.0‰ to 14.4‰) than those for summer samples (-3.7‰ to 8.6‰) might be due to more contributions from coal combustion in winter. Coal combustion (31% ± 9%, 25% ± 9% in winter and summer, respectively) and biomass burning (30% ± 12%, 36% ± 12% in winter and summer, respectively) were the main sources using Bayesian mixing model. These results provided clear evidence of particulate nitrate formation and sources under different PM_2.5 levels, and aided in reducing atmospheric nitrate in urban environments.     

2011年秋季广州城区大气PM2.5微观形貌和粒度分布

利用高分辨率扫描电镜加能谱仪(SEM-EDX)和图像数据分析技术对2011年秋季广州市中心大气PM2.5的微观形貌和粒度分布特征进行研究,系统获得3种典型颗粒(矿物、烟尘集合体和燃煤飞灰)和其它未知颗粒的数量-粒度分布和体积-粒度分布数据.结果表明,PM2.5颗粒数量-粒度分布峰值落在0.1~0.2μm之间,属于积聚模态中含有气相反应产物的凝结亚模态.3种典型颗粒对PM2.5的数量和体积贡献均为矿物>>烟尘集合体>飞灰.矿物主要分布在0.1~0.3μm范围内,所占数量百分比为41.97%,其中0.1~0.2μm范围内矿物占比高达26.42%,是影响PM2.5颗粒整体分布的主要因素.不同采样时段(上午、下午、晚上)和下雨前后PM2.5颗粒的粒度分布特征基本一致,但晚上和下雨后小于0.1μm的颗粒比例有明显减少趋势.     

铜陵市冬春季PM10和PM2.5中元素特征及来源分析

目的了解铜陵市颗粒物中的元素特征和主要来源。方法选择2014年冬季和春季的部分时段,在铜陵市国家环境空气监测站——新民污水处理厂(工业区)采集PM_(10)和PM_(2.5)样品,使用X射线荧光光谱(XRF)法进行元素的定量测试。采样期间,冬季的空气质量以良和中、轻度污染为主;春季以中度和重度污染天气为主,采样期间出现了明显的重污染。结果 PM_(2.5)和PM_(10)中S和Si元素的浓度均比其余元素高,P和Cu元素的浓度远低于其余元素。空气污染的指数越高,Fe、Mg、Al、Si则更易富集在PM_(10)上,而K、Cu、Na、Cl、S元素更易富集在PM_(2.5)上,Ca和P这两种元素在PM_(10)和PM_(2.5)上的富集程度相当。空气颗粒物中,富集最多的元素是K,其次为Fe和Mg;元素Cu、K、Cl在PM_(10)中的富集程度要高于PM_(2.5)。结论扬尘(包括地面扬尘和建筑尘)是PM_(10)的最大来源,其次是开采矿山和燃烧生物质,燃煤、炼铜等工企业排放贡献最小;对于PM_(2.5)而言,最大的来源是风沙、扬尘和开采矿山,其次是燃煤、燃烧生物质和其他的工企业排放,炼铜的贡献最小。     

Progressively narrow the gap of PM2.5 pollution characteristics at urban and suburban sites in a megacity of Sichuan Basin, China

Nowadays, the fine particle pollution is still severe in some megacities of China, especially in the Sichuan Basin, southwestern China. In order to understand the causes, sources, and impacts of fine particles, we collected PM_2.5 samples and analyzed their chemical composition in typical months from July 2018 to May 2019 at an urban and a suburban (background) site of Chengdu, a megacity in this region. The daily average concentrations of PM_2.5 ranged from 5.6-102.3 µg/m³ and 4.3-110.4 µg/m³ at each site. Secondary inorganics and organic matters were the major components in PM_2.5 at both sites. The proportion of nitrate in PM_2.5 has exceeded sulfate and become the primary inorganic component. SO₂ was easier to transform into sulfate in urban areas because of Mn-catalytic heterogeneous reactions. In contrast, NO₂ was easily converted in suburbs with high aerosol water content. Furthermore, organic carbon in urban was much greater than that in rural, other than elemental carbon. Element Cr and As were the key cancer risk drivers. The main sources of PM_2.5 in urban and suburban areas were all secondary aerosols (42.9%, 32.1%), combustion (16.0%, 25.2%) and vehicle emission (15.2%, 19.2%). From clean period to pollution period, the contributions from combustion and secondary aerosols increased markedly. In addition to tightening vehicle controls, urban areas need to restrict emissions from steel smelters, and suburbs need to minimize coal and biomass combustion in autumn and winter.     

Seasonal variations, temperature dependence, and sources of size-resolved PM components in Nanjing, east China

Size-segregated ambient particulate matter (PM) samples were collected seasonally in suburban Nanjing of east China from 2016 to 2017 and chemically speciated. In both fine (< 2.1 µm, PM_2.1) and coarse (> 2.1 µm, PM_>2.1) PM, organic carbon (OC) accounted for the highest fractions (26.9% ± 10.9% and 23.1% ± 9.35%) of all measured species, and NO₃⁻ lead in average concentrations of water-soluble inorganic ions (WSIIs). The size distributions of measured components were parameterized using geometric mean diameter (GMD). GMD values of NO₃⁻, Cl⁻, OC, and PM for the whole size range varied from < 2.1 µm in winter to > 2.1 μm in warm seasons, which was due to the fact that the size distributions of semi-volatile components (e.g., NH₄NO₃, NH₄Cl, and OC) had a dependency on the ambient temperature. Unlike OC, elemental carbon (EC), and elements, NH₄⁺, NO₃⁻, and SO₄²⁻ exhibited an increase trend in GMD values with relative humidity, indicating that the hygroscopic growth might also play a role in driving seasonal changes of PM size distributions. Positive matrix factorization was performed using compositional data of fine and coarse particles, respectively. The secondary formation of inorganic salts contributing to the majority (> 70%) of fine PM and 20.2% ± 19.9% of speciated coarse PM. The remaining coarse PM content was attributed to a variety of dust sources. Considering that coarse and fine PM had comparable mass concentrations, more attention should be paid to local dust emissions in future air quality plans.     

Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China

The intraurban distribution of PM_2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM_2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM_2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R² compared with cross-validation results. For MLR models, inflation of both R² and R²_CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_2.5 levels. Inflated within-sample R² also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_2.5 concentrations.     

Chemical formation and source apportionment of PM2.5 at an urban site at the southern foot of the Taihang mountains

The region along the Taihang Mountains in the North China Plain (NCP) is characterized by serious fine particle pollution. To clarify the formation mechanism and controlling factors, an observational study was conducted to investigate the physical and chemical properties of the fine particulate matter in Jiaozuo city, China. Mass concentrations of the water-soluble ions (WSIs) in PM_2.5 and gaseous pollutant precursors were measured on an hourly basis from December 1, 2017, to February 27, 2018. The positive matrix factorization (PMF) method and the FLEXible PARTicle (FLEXPART) model were employed to identify the sources of PM_2.5. The results showed that the average mass concentration of PM_2.5 was 111 μg/m³ during the observation period. Among the major WSIs, sulfate, nitrate, and ammonium (SNA) constituted 62% of the total PM_2.5 mass, and NO₃^? ranked the highest with an average contribution of 24.6%. NH₄⁺ was abundant in most cases in Jiaozuo. According to chemical balance analysis, SO₄^2?, NO₃^?, and Cl^? might be present in the form of (NH₄)₂SO₄, NH₄NO₃, NH₄Cl, and KCl. The liquid-phase oxidation of SO₂ and NO₂ was severe during the haze period. The relative humidity and pH were the key factors influencing SO₄^2- formation. We found that NO₃^? mainly stemmed from homogeneous gas-phase reactions in the daytime and originated from the hydrolysis of N₂O₅ in the nighttime, which was inconsistent with previous studies. The PMF model identified five sources of PM_2.5: secondary origin (37.8%), vehicular emissions (34.7%), biomass burning (11.5%), coal combustion (9.4%), and crustal dust (6.6%).     

An integrative analysis of miRNA and mRNA expression in the brains of Alzheimer's disease transgenic mice after real-world PM2.5 exposure

Fine particulatematter (PM2.5) is associated with increased risks of Alzheimer’s disease (AD),yet the toxicologicalmechanisms of PM2.5 promoting AD remain unclear. In this study,wildtype and APP/PS1 transgenic mice (AD mice) were exposed to either filtered air (FA) or PM2.5 for eight weeks with a real-world exposure system in Taiyuan, China (mean PM2.5 concentration in the cage was 61 μg/m3). We found that PM2.5 exposure could remarkably aggravate AD mice’s ethological and brain ultrastructural damage, along with the elevation of the pro-inflammatory cytokines (IL-6 and TNF-α), Aβ-42 and AChE levels and the decline of ChAT levels in the brains. Based on high-throughput sequencing results, some differentially expressed (DE) mRNAs and DE miRNAs in the brains of AD mice after PM2.5 exposure were screened.Using RT-qPCR, seven DEmiRNAs (mmu-miR-193b-5p, 122b-5p, 466h-3p, 10b-5p, 1895, 384–5p, and 6412) and six genes (Pcdhgb8, Unc13b, Robo3, Prph, Pter, and Tbata) were evidenced the and verified. Two miRNA-target gene pairs (miR-125b-Pcdhgb8 pair and miR-466h-3p-IL-17Rα/TGF-βR2/Aβ-42/AChE pairs) were demonstrated that they were more related to PM2.5-induced brain injury. Results of Gene Ontology (GO) pathways and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways predicted that synaptic and postsynaptic regulation, axon guidance, Wnt, MAPK, and mTOR pathways might be the possible regulatory mechanisms associated with pathological response. These revealed that PM2.5- elevated pro-inflammatory cytokine levels and PM2.5-altered neurotransmitter levels in AD mice could be the important causes of brain damage and proposed the promising miRNA andmRNA biomarkers and potentialmiRNA-mRNA interaction networks of PM2.5-promoted AD.     

Revealing the characteristics of dissolved organic matter in urban runoff at three typical regions via optical indices and molecular composition

Dissolved organic matter (DOM) plays a major role in ecological systems and influences the fate and transportation of many pollutants. Despite the significance of DOM, understanding of how environmental and anthropogenic factors influence its composition and characteristics is limited, especially in urban stormwater runoff. In this article, the chemical properties (pollutant loads, molecular weight, aromaticity, sources, and molecular composition) of DOM in stormwater extracted from three typical end-members (traffic, residential, and campus regions) were characterized by UV–visible (UV–vis) spectroscopy, excitation-emission matrix spectroscopy combined with parallel factor analysis (EEM-PARAFAC), and ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). There are three findings: (1) The basic properties of DOM in stormwater runoff varied obviously from three urban fields, and the effect of initial flush was also apparent. (2) The DOM in residential areas mainly came from autochthonous sources, while allochthonous sources primarily contributed to the DOM in traffic and campus areas. However, it was mainly composed of terrestrial humic-like components with CHO and CHON element composition and HULO and aliphatic formulas. (3) The parameters characterizing DOM were primarily related to terrestrial source and aromaticity, but their correlations varied. Through the combination of optical methods and UPLC-Q-TOF spectrometry, the optical and molecular characteristics of rainwater are effectively revealed, which may provide a solid foundation for the classification management of stormwater runoff in different urban regions.     

Water-soluble matter in PM2.5 in a coastal city over China: Chemical components, optical properties, and source analysis

Although marine and terrestrial emissions simultaneously affect the formation of atmospheric fine particles in coastal areas, knowledge on the optical properties and sources of water-soluble matter in these areas is still scarce. In this work, taking Qingdao, China as a typical coastal location, the chemical composition of PM_2.5 during winter 2019 was analyzed.Excitation-emission matrix fluorescence spectroscopy was combined with parallel factor analysis model to explain the component...     

Study of aerosol characteristics and sources using MAX-DOAS measurement during haze at an urban site in the Fenwei Plain

Atmospheric aerosols have effects on atmospheric radiation assessments, global climate change, local air quality and visibility. In particular, aerosols are more likely transformed and accumulated in winter. In this paper, we used the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument to study the characteristics of aerosol type and contributions of PM_2.5 chemical components to aerosol extinction (AE), vertical distribution of aerosols, and source. From December 30, 2018 to January 27, 2019, we conducted MAX-DOAS observations on Sanmenxia. The proportion of PM_2.5 to PM₁₀ was 69.48%–95.39%, indicating that the aerosol particles were mainly fine particles. By analyzing the ion data and modifying Interagency Monitoring of Protected Visual Environments (IMPROVE) method, we found that nitrate was the largest contributor to AE, accounting for 31.51%, 28.98%, and 27.95% of AE on heavily polluted, polluted, and clean days, respectively. NH₄⁺, OC, and SO₄^2? were also major contributors to AE. The near-surface aerosol extinction retrieved from MAX-DOAS measurement the PM_2.5 and PM₁₀ concentrations measured by an Unmanned Aerial Vehicle (UAV) have the same trend in vertical distribution. AE increased about 3 times from surface to 500 m. With the backward trajectory of the air mass during the haze, we also found that the continuous heavy pollution was mainly caused by transport of polluted air from the northeast, then followed by local industrial emissions and other sources of emissions under continuous and steady weather conditions.