Atmospheric Fine Particulate Matter Research Articles

Characterization of cross-continental PM2.5: Insights into emissions and chemical composition

Atmospheric fine particulate matter (PM2.5) is a critical indicator of air quality, with substantial implications for human health. Understanding the emission sources and chemical composition of PM2.5 is crucial for mitigating possible adverse health effects. This study spans five diverse cities on three continents from north and south hemisphere: Stockholm (Sweden), Kyoto (Japan), Limeira, Ribeirão Preto, and Cáceres (Brazil). Our objective was to assess PM2.5 chemical composition and regional and long-range transport influences to identify the main sources of particulate air pollution at these cities during the winter/dry seasons. All studied cities but Kyoto exhibited PM2.5 levels above World Health Organization (WHO) guidelines, with the Brazilian cities experiencing the highest fine particle pollution levels, implying increased adverse health risks. We observed significant variations in concentrations of polycyclic aromatic compounds (PACs), monosaccharide anhydrides (MAs), and inorganic elements. Limeira exhibited the highest levels of total PACs (median level of 12.4 ng m−3), while Cáceres displayed high variability of PACs, most likely due to episodic regional wildfire events. MA concentrations were significantly higher in Limeira and Ribeirão Preto and together with elevated levels of retene and potassium (K) they suggested a substantial influence of biomass burning. Backward air mass trajectory analysis suggested widespread Amazon and Savanna wildfires along with local fires as main contributors for these sites. All source identification approaches highlighted differences among the cities, with Stockholm and Kyoto showing influence of sources related to traffic emissions, waste burning, and long-range transport, and Brazilian cities traffic, industrial, biogenic, and more evident biomass burning. This cross-continental study provides valuable insights into PM2.5 composition and emission sources, emphasizing the impact of different emissions on air quality. Our findings underscore the importance of local strategies to mitigate air pollution and protect public health, especially in regions where PM2.5 levels consistently exceed recommended guidelines.

Characteristics and Source Analysis of Carbonaceous Aerosols in PM2.5 in Huaxi District, Guiyang

Carbonaceous aerosol, as an important component of atmospheric aerosol, has a significant impact on atmospheric environmental quality, human health, and global climate change. To investigate the characteristics and sources of carbonaceous aerosol in atmospheric fine particulate matter (PM2.5) in Huaxi District of Guiyang, an in-situ observational study was conducted during different seasons in 2020, and the carbonaceous components of PM2.5 were measured using a thermal-optical carbon analyzer (DRI Model 2015). The results of the study showed that the average concentrations of PM2.5, total carbonaceous aerosol (TCA), organic carbon (OC), secondary organic carbon (SOC), and elemental carbon (EC) concentrations during the observation period were (39.7±22.3), (14.1±7.2), (7.6±3.9), (4.4±2.6), and (2.0±1.0) μg·m-3, respectively, and the mean value of OC/EC was (3.9±0.8). ρ(PM2.5), ρ(TCA), ρ(OC), ρ(SOC), and ρ(EC) showed a seasonal variation pattern with the highest in winter [(52.6±28.6), (17.0±9.6), (9.1±5.2), (6.1±3.9), and (2.4±1.2) μg·m-3, respectively] and the lowest in summer [(25.1±7.1), (11.6±3.6), (6.3±1.9), (3.7±1.2), and (1.6±0.6) μg·m-3, respectively]. The seasonal variation in OC/EC showed summer (4.2±0.8) > winter (3.8±0.9) > autumn (3.8±0.5) > spring (3.7±0.9), indicating the presence of SOC generation in all seasons in Huaxi District. SOC showed a significant correlation with OC (R2 =0.9), and the SOC concentration tended to increase with the increase in atmospheric oxidation. OC showed a good correlation with EC in all seasons, with the highest in autumn (R2 =0.9) and lower correlations in the other three seasons (R2 ranged from 0.74 to 0.75), indicating a common source. According to OC/EC ratio range, it was preliminarily determined that carbonaceous aerosol came from vehicle exhaust emissions, coal burning emissions, and biomass combustion emissions. In order to further quantify the contribution of major emission sources to carbonaceous aerosol, the results of this study using PMF to analyze the sources of carbonaceous aerosol showed that the main sources of carbonaceous aerosol in Huaxi District of Guiyang were coal combustion sources (29.3%), motor vehicle emission sources (21.5%), and biomass combustion sources (49.2%).

Atmospheric fine particulate matter (PM2.5) induces pulmonary fibrosis by regulating different cell fates via autophagy

The presence of respiratory diseases demonstrates a positive correlation with atmospheric fine particulate matter (PM2.5) exposure. The respiratory system is the main target organ affected by PM2.5, and exposure to PM2.5 elevates the likelihood of developing pulmonary fibrosis (PF). In this study, lung epithelial cell (BEAS-2B) and fibroblast (NIH-3T3) were used as in vitro exposure models to explore the mechanisms of PF. PM2.5 exposure caused mitochondrial damage in BEAS-2B cells and increased a fibrotic phenotype in NIH-3T3 cells. Epithelial cells and fibroblasts have different fates after PM2.5 exposure due to their different sensitivities to trigger autophagy. Exposure to PM2.5 inhibits mitophagy in BEAS-2B cells, which hinders the removal of damaged mitochondria and triggers cell death. In this process, the nuclear retention of the mitophagy-related protein Parkin prevents it from being recruited to mitochondria, resulting in mitophagy inhibition. In contrast, fibroblasts exhibit increased levels of autophagy, which may isolate PM2.5 and cause abnormal fibroblast proliferation and migration. Fibrotic phenotypes such as collagen deposition and increased α-actin also appear in fibroblasts. Our results identify PM2.5 as a trigger of PF and delineate the molecular mechanism of autophagy in PM2.5 induced PF, which provides new insights into the pulmonary injury.

Functional identification of long non-coding RNAs induced by PM2.5 in microglia through microarray analysis

As a dominating air pollutant, atmospheric fine particulate matter within 2.5 μm in diameter (PM2.5) has attracted increasing attention from the researchers all over the world, which will lead to various adverse effects on the central nervous system (CNS), yet the potential mechanism is unclear. In this study, the microglia (BV2 cell line) were exposed to different concentrations of PM2.5 (5, 10 and 20 μg/cm2) for 24 h. It was found that PM2.5 could result in adverse effects on microglia such as decreased cell viability, structural damage and even cell death. And it was reported that long non-coding RNAs (lncRNAs) could participate in multitudinous neurological diseases. Therefore, the microarray analysis was conducted in order to disclose the underlying neurotoxicity mechanism of PM2.5 by ascertaining the differentially expressed lncRNAs (DElncRNAs). The consequences indicated that the DElncRNAs were enriched in various biological pathways, including ferroptosis, IL-17 signaling pathway and NOD-like receptor signaling pathway. Moreover, the cis- and trans-regulated mRNAs by DElncRNAs as well as the corresponding transcriptional factors (TFs) were observed, such as CEBPA, MYC, MEIS1 and KLF4. In summary, our study supplies some candidate libraries and potential preventive target against PM2.5-induced toxicity through targeting lncRNAs. Furthermore, the post-transcriptional regulation will contribute to the future research on PM2.5-induced neurotoxicity.

Insights into the seasonal characteristics of single particle aerosols in Chengdu based on SPAMS

To investigate the seasonal characteristics in air pollution in Chengdu, a single particle aerosol mass spectrometry was used to continuously observe atmospheric fine particulate matter during one-month periods in summer and winter, respectively. The results showed that, apart from O3, the concentrations of other pollutants (CO, NO2, SO2, PM2.5 and PM10) were significantly higher in winter than in summer. All single particle aerosols were divided into seven categories: biomass burning (BB), coal combustion (CC), Dust, vehicle emission (VE), K mixed with nitrate (K-NO3), K mixed with sulfate and nitrate (K-SN), and K mixed with sulfate (K-SO4) particles. The highest contributions in both seasons were VE particles (24%). The higher contributions of K-SO4 (16%) and K-NO3 (10%) particles occurred in summer and winter, respectively, as a result of their different formation mechanisms. S-containing (K-SO4 and K-SN), VE, and BB particles caused the evolution of pollution in both seasons, and they can be considered as targets for future pollution reduction. The mixing of primary sources particles (VE, Dust, CC, and BB) with secondary components was stronger in winter than in summer. In summer, as pollution worsens, the mixing of primary sources particles with 62 [NO3]− weakened, but the mixing with 97 [HSO4]− increased. However, in winter, the mixing state of particles did not exhibit an obvious evolution rules. The potential source areas in summer were mainly distributed in the southern region of Sichuan, while in winter, besides the southern region, the contribution of the western region cannot be ignored.

Acute exposure to seasonal PM2.5 induces toxicological responses in A549 cells cultured at the air-liquid interface mediated by oxidative stress and endoplasmic reticulum stress

Atmospheric fine particulate matter (PM2.5) enters the human body through respiration and poses a threat to human health. This is not only dependent on its mass concentration in the atmosphere, but also related to seasonal variations in its chemical components, which makes it important to study the cytotoxicity of PM2.5 in different seasons. Traditional immersion exposure cannot simulate the living environment of human epithelial cells in the human body, making this method unsuitable for evaluating the inhalation toxicity of PM2.5. In this study, a novel air-liquid interface (ALI) particulate matter exposure device (VITROCELL Cloud 12 system) was used to evaluate the toxic effects and potential mechanisms of human lung epithelial cells (A549) after exposure to seasonal PM2.5. PM2.5 samples from four seasons were collected and analyzed for chemical components. After 6 h of exposure to seasonal PM2.5, winter PM2.5 exhibited the highest cytotoxicity among most toxicity indicators, especially apoptosis rate, reactive oxygen species (ROS), inflammatory responses and DNA damage (γ-H2AX). The effect of autumn PM2.5 on apoptosis rate was significantly higher than that in spring, and there was no significant difference in other toxicity indicators between spring and autumn. The cytotoxicity of summer PM2.5 was the lowest among the four seasons. It should be noted that even exposure to low doses of summer PM2.5 leads to significant DNA damage in A459 cells. Correlation analysis results showed that water-soluble ions, metallic elements, and polycyclic aromatic hydrocarbons (PAHs) were associated with most toxicological endpoints. Inhibitors of oxidative stress and endoplasmic reticulum (ER) stress significantly inhibited cellular damage, indicating that PM2.5-induced cytotoxicity may be related to the generation of ROS and ER stress. In addition, PM2.5 can induce ER stress through oxidative stress, which ultimately leads to apoptosis.

Study on the Concentration of Top Air Pollutants in Xuzhou City in Winter 2020 Based on the WRF-Chem and ADMS-Urban Models

In recent years, the issue of air pollution has garnered significant public attention globally, with a particular emphasis on the challenge of atmospheric fine particulate matter (PM2.5) pollution. The efficient and precise simulation of changes in pollutant concentrations, as well as their spatial and temporal distribution, is essential for effectively addressing the air pollution issue. In this paper, the WRF-Chem model is used to simulate the meteorological elements including temperature (T), relative humidity (RH), wind speed (WS), and pressure (P), and the concentrations of PM2.5 and PM10 atmospheric pollutants in December 2020 in Xuzhou City. Simultaneously, the ADMS-Urban model was employed to conduct a higher spatial resolution study of PM2.5 concentrations during the heavy pollution days of 11–12 December 2020 in Xuzhou City. The study shows that the WRF-Chem model can simulate the meteorological conditions of the study time period better, and the correlation coefficients (R) of pressure, temperature, wind speed, and relative humidity are 0.99, 0.87, 0.75, and 0.70, respectively. The WRF-Chem model can accurately simulate the PM2.5 concentration on clean days (R of 0.66), but the simulation of polluted days is not satisfactory. Therefore, the ADMS-Urban model was chosen to simulate the PM2.5 concentration on polluted days in the center of Xuzhou City. The ADMS-Urban model can simulate the distribution characteristics and concentration changes of PM2.5 around roads and buildings in the center of Xuzhou City. Comparing the simulation results of the two models, it was found that the two models have their own advantages in PM2.5 concentration simulation, and how to better couple the two models is the next research direction.

Chemically specific sampling bias: the ratio of PM2.5 to surface AOD on average and peak days in the U.S.

Accurate quantitative description of the atmospheric fine particulate matter (PM2.5) burden requires an understanding of aerosol amounts and physicochemical properties that transcends measurement platforms.

Long-term exposure to ambient air pollution and inflammatory response in the KORA study

Previous studies consistently showed an association between fine atmospheric particulate matter (PM2.5) and cardiovascular diseases. Concerns about adverse health effects of ultrafine particles (UFP) are growing but long-term studies are still scarce. In this study, we examined the association between long-term exposure to ambient air pollutants and blood biomarkers of inflammation and coagulation, including fibrinogen, high-sensitivity C-reactive protein (hs-CRP), serum amyloid A (SAA) adiponectin and interleukin-6 (IL-6), measured in the German KORA-S4 cohort study (1999–2001). IL-6 was available for older participants only, who were therefore considered as a subsample. Annual mean concentrations of UFP (as particle number concentration), particulate matter in different particles sizes (PM10, PMcoarse, PM2.5, PM2.5 absorbance), ozone (O3), and nitrogen oxides (NO2, NOX) were estimated by land-use regression models and assigned to participants' home addresses. We performed a multiple linear regression between each pollutant and each biomarker with adjustment for confounders. Per 1 interquartile range (IQR, 1945 particles/cm3) increase of UFP, fibrinogen increased by 0.70 % (0.04; 1.37) and hs-CRP increased by 3.16 % (−0.52; 6.98). Adiponectin decreased by −2.53 % (−4.78; −0.24) per 1 IQR (1.4 μg/m3) increase of PM2.5. Besides, PM2.5 was associated with increased IL-6 in the subsample. In conclusion, we observed that long-term exposure to air pollutants, including both fine and ultrafine particles, was associated with higher concentrations of pro-inflammatory and lower concentrations of an anti-inflammatory blood biomarkers, which is consistent with an increased risk for cardiovascular disease observed for long-term exposure to air pollutants.

Key toxic components and sources affecting oxidative potential of atmospheric particulate matter using interpretable machine learning: Insights from fog episodes

Fog significantly affects the air quality and human health. To investigate the health effects and mechanisms of atmospheric fine particulate matter (PM2.5) during fog episodes, PM2.5 samples were collected from the coastal suburb of Qingdao during different seasons from 2021 to 2022, with the major chemical composition in PM2.5 analyzed. The oxidative potential (OP) of PM2.5 was determined using the dithiothreitol (DTT) method. A positive matrix factorization model was adopted for PM2.5. Interpretable machine learning (IML) was used to reveal and quantify the key components and sources affecting OP. PM2.5 exhibited higher oxidative toxicity during fog episodes. Water-soluble organic carbon (WSOC), NH4+, K+, and water-soluble Fe positively affected the enhancement of DTTV (volume-based DTT activity) during fog episodes. The IML analysis demonstrated that WSOC and K+ contributed significantly to DTTV, with values of 0.31 ± 0.34 and 0.27 ± 0.22 nmol min−1 m−3, respectively. Regarding the sources, coal combustion and biomass burning contributed significantly to DTTV (0.40 ± 0.38 and 0.39 ± 0.36 nmol min−1 m−3, respectively), indicating the significant influence of combustion-related sources on OP. This study provides new insights into the effects of PM2.5 compositions and sources on OP by applying IML models.

Quantifying metallic components in aerosol filter samples using micro-synchrotron radiation X-ray fluorescence: With quartz filter as an example

Metals in atmospheric fine particulate matter (PM2.5) are presumed to have toxic effects. For environmental epidemiological studies, measuring the metallic components of PM2.5 collected by personal samplers and quartz fiber filters (QFF) has two key advantages: reducing exposure misclassification and enabling analyses of multiple (organic and inorganic) components on a single filter. We developed a sensitive, low-sample-consumption method to quantify the metallic components of PM2.5 collected on QFFs using micro-synchrotron radiation X-ray fluorescence (μ-SRXRF). Teflon filter is used here as a reference to test the substrate applicability of QFF. This method requires only a 5-mm-diameter portion of a QFF and can simultaneously quantify 15 metals with atomic numbers Z = 19–34 (i.e., K–Se, excluding Sc). The analysis time for each sample was 120–360 s, and the method detection limits were 0.04–36.6 ng/m3. We validated this method by performing cross-comparisons with two external methods: offline inductively coupled plasma mass spectrometry (ICP-MS) and online X-ray fluorescence (XRF). The slopes of the linear fits between the μ-SRXRF and offline ICP-MS methods varied from 0.40 to 1, with R2 values ranging from 0.62 to 0.98. The slopes of the linear fits between the μ-SRXRF and online XRF methods were between 0.29 and 0.89, with R2 values ranging from 0.39 to 0.80. We applied the proposed method to analyze 391 personal PM2.5 samples collected in an epidemiological study conducted in urban Beijing. By comparing the results with the ambient concentrations from a fixed monitoring site, we found that the concentrations of some metallic components in personal PM2.5 samples were significantly different from those in the corresponding fixed-site samples, with up to an order of magnitude difference. Because personal sampling can reduce the potential for exposure misclassifications, the high-resolution temporal analysis of metallic components in personal samples using the μ-SRXRF method could be a useful approach for exploring the health risks and identifying the sources of metallic components of PM2.5.

Insights the dominant contribution of biomass burning to methanol-soluble PM2.5 bounded oxidation potential based on multilayer perceptron neural network analysis in Xi'an, China

Atmospheric fine particulate matter (PM2.5) is associated with cardiorespiratory morbidity and mortality due to its ability to generate reactive oxygen species (ROS). Ambient PM2.5 samples were collected during heating and nonheating seasons in Xi'an, China, and the ROS-generation potential of PM2.5 was quantified using the dithiothreitol (DTT) assay. Additionally, positive matrix factorization combined with multilayer perceptron was employed to apportion sources contributing to the oxidation potential of PM2.5. Both the mass concentration of PM2.5 and the volume-based DTT activity (DTTv) were higher during the heating season than during the nonheating season. The primary contributors to DTTv were combustion (biomass and coal) sources during the heating season (>52 %), whereas secondary formation dominated DTT activity during the nonheating season (35.7 %). In addition, the secondary reaction process promoted the generation of intrinsic oxidation potential (OP) of sources. Among all the sources investigated (traffic source, industrial emission, mineral dust, biomass burning, secondary formation and coal combustion), the inherent oxidation potential of biomass burning was the highest, whereas that of mineral dust was the lowest. Our study indicates that anthropogenic sources, especially biomass burning, should be prioritized in PM2.5 toxicity control strategies.

Spatial patterns of PM2.5-bound heavy metals and analysis of their influencing factors in China

This study investigates the relationship between the spatial distribution patterns of heavy metals (HMs) in atmospheric fine particulate matter (PM2.5) and their influencing factors in China, in order to address air pollution problems. Using HM data from PM2.5 collected from 88 Chinese cities between 2012 and 2020, the study employed a combination of enrichment factor (EF) analysis, standard deviation ellipses and hot spot analysis to assess spatial distribution patterns; geographical detectors to identify influencing factors; and hybrid single-particle Lagrangian integrated trajectory models (HYSPLIT) to determine air mass movement. The results reveal that the spatial distribution of PM2.5-bound HMs in China is characterized by high levels in the north and interior and low levels in the south and along the coast. Anthropogenic activities affect HM levels in China's interior more strongly than on the coast. Moreover, the distribution of PM2.5-bound HMs in China exhibits a northeast-southwest spatial pattern, with significantly higher levels observed in central and northern regions than in eastern regions, particularly along the coast. Industrial waste emissions and energy consumption are key factors contributing to high PM2.5-bound HMs. In central China, the summer air mass movement predominantly follows a southeast direction, while in winter, it follows a northwest direction, originating from inland. In contrast, in eastern China, summer air mass movement predominantly follows a southeast direction, while in winter, it follows a northeast direction, originating from the ocean. Overall, our findings suggest that HMs in PM2.5 display high clustering values in central China, due primarily to industrial waste emissions, energy consumption, and air mass movement.

Concentration identification and endpoint-oriented health risk assessments on a broad-spectrum of organic compounds in atmospheric fine particles: A sampling experimental study in Beijing, China

Understanding the complicate chemical components in atmospheric fine particulate matter (PM2.5) helps policy makers for pollutants control track progress and identify disparities in overall health risks. However, till now, information on accurate component detection, source identification, and effect-oriented risk assessment is scarce, especially for the simultaneous analysis of a broad-spectrum of compounds. In this study, a high-throughput target method was employed to distinguish the occurrence and characteristics of 152 chemicals: phthalate esters (PAEs), organophosphate esters (OPEs), carboxylic acid esters (CAEs), nitrophenols (NPs), nitrogen heterocyclic compounds (NHCs), per- and poly-fluoroalkyl substances (PFASs), triclosan and its derivatives (TCSs), and organosulfates (OSs) in ambient PM2.5 collected from Beijing, China. Detection frequencies of 77 targeted compounds were >50 %. Total concentrations of all compounds ranged from 33.1 to 745 ng/m3. The median concentration of ∑PAEs (108 ng/m3) was the highest, followed by ∑CAEs (12.2 ng/m3) and ∑NPs (10.1 ng/m3). Organophosphate diesters (di-OPEs) and TCSs were reported for the first time in ambient PM2.5. The pollutants mainly originated from the local industrial production, release of building materials, and environmental degradation of parent compounds. Based on absorption, distribution, metabolism, excretion, and toxicity (ADMET)-oriented risk evaluations, we found that bis (2-ethylhexyl) phthalate, diisobutyl phthalate, dibutyl phthalate, and di (2-ethylhexyl) adipate have high health risks. Additionally, the high oxidative stress potential of 4-nitrocatechol and the strong blood-brain barrier penetration potential of triclosan cannot be ignored. Our study will facilitate the evaluations of specific health outcomes and mechanisms of pollutants, and suggestion of pollutants priority control to reduce human health hazards caused by atmospheric particles.

Atmospheric fine particulate matter (PM2.5) in Bloemfontein, South Africa

Atmospheric fine particulate matter (PM2.5) in Bloemfontein, South Africa

Combined Smog Chamber/Oxidation Flow Reactor Study on Aging of Secondary Organic Aerosol from Photooxidation of Aromatic Hydrocarbons.

Secondary organic aerosol (SOA) is a significant component of atmospheric fine particulate matter (PM2.5), and their physicochemical properties can be significantly changed in the aging process. In this study, we used a combination consisting of a smog chamber (SC) and oxidation flow reactor (OFR) to investigate the continuous aging process of gas-phase organic intermediates and SOA formed from the photooxidation of toluene, a typical aromatic hydrocarbon. Our results showed that as the OH exposure increased from 2.6 × 1011 to 6.3 × 1011 molecules cm-3 s (equivalent aging time of 2.01-4.85 days), the SOA mass concentration (2.9 ± 0.05-28.7 ± 0.6 μg cm-3) and corrected SOA yield (0.073-0.26) were significantly enhanced. As the aging process proceeds, organic acids and multiple oxygen-containing oxidation products are continuously produced from the photochemical aging process of gas-phase organic intermediates (mainly semi-volatile and intermediate volatility species, S/IVOCs). The multigeneration oxidation products then partition to the aerosol phase, while functionalization of SOA rather than fragmentation dominated in the photochemical aging process, resulting in much higher SOA yield after aging compared to that in the SC. Our study indicates that SOA yields as a function of OH exposure should be considered in air quality models to improve SOA simulation, and thus accurately assess the impact on SOA properties and regional air quality.

Meteorology-driven PM2.5 interannual variability over East Asia

Atmospheric fine particulate matter (PM2.5) is a human health risk factor, but its ambient concentration depends on both precursor emissions and meteorology. While emission reductions are used to set PM2.5-related health policies, the effect of meteorology is often overlooked. To explore this aspect, we examined PM2.5 interannual variability (IAV) associated with meteorological parameters using the long-term simulation from the Community Earth System Model (CESM1), a global climate-chemistry model, with fixed emissions. The results are subsequently contrasted with the MERRA-2 reanalysis dataset, which inherently considers emission and meteorology effects. Over continental East Asia, the CESM1 domain-average PM2.5 IAV is 6.7 %, mainly attributed to humidity, precipitation, and ventilation variation. The grid-cell PM2.5 IAVs over southern East China are larger, up to 12 % due to the more substantial influence of El Niño-induced meteorological anomalies. Under such climate extreme, sub-regional PM2.5 concentration may occasionally exceed WHO air quality guideline levels despite the compliance of the long-term mean. The simulated PM2.5 IAV over continental East Asia is ~25 % of that derived from the MERRA-2 data, which highlights the influence of both emission and meteorology-driven variations and trends inherent in the latter. Although emission-driven variability is significant to PM2.5 IAV, in remote areas downwind of major source regions in East Asia, North America, and Western Europe, the MERRA-2 data revealed that meteorological variations contributed more to PM2.5 IAV than emission variations. Thus, when setting policies for complying with the WHO PM2.5-related air quality guideline levels, the highest annual PM2.5 associated with climate extremes should be considered instead of that based on average climate conditions.

Exploration of spatial heterogeneity in the effect of woodland proportion on PM2.5: An empirical study from the Yangtze River economic Belt, China

Woodland is the most important land cover type for enhancing the environmental governance capacity and ecological protection benefits. Woodland proportion is crucial in reducing the quality and concentration of atmospheric fine particulate matter (PM2.5) and improving the atmospheric environment. Existing studies have mostly examined the connection between the woodland proportion and PM2.5 on a global scale, while ignoring the geographical variability and differences in the scope and intensity of the effect. Therefore, this study investigated the regional features of the effect from the perspective of spatial heterogeneity, using a spatial agglomeration analysis method, and global and local regression analyses models with 1049 counties in the Yangtze River Economic Belt (YREB). The studies reveal that woodland proportion has a considerable impact on PM2.5, and the effect’s direction is identical to that of the expectation at the global scale. The PM2.5 concentration falls by 0.017% for every 1% increase in the woodland proportion. The influence of woodland proportion on PM2.5 showed noticeable spatial heterogeneity, and the woodland proportion in 794 counties is strongly associated with PM2.5 concentration in local areas, with 66.44% of counties having the most obvious negative effect, and the areas with the greatest effect intensity are mostly distributed in mountainous areas. The concentration of PM2.5 in the YREB exhibiting a prominent characteristics of local spatial correlation and agglomeration, and a spatial characteristics of eastward peak and an westward low. Contrastingly, the distribution of the woodland proportion were the opposite—eastward high and westward low—with noticeable spatial variability. The findings of this study offer a foundation for decision-making and theoretical support for national or regional implementation of regionally differentiated and precise measures to prevent and reduce air pollution.

Application of degradation technology of light-emitting diode and auto-monitoring system for harmful substances in farrowing areas of a specific pathogen-free pig farm

In pig farms, disinfectants are applied for the effective cleaning and disinfection regimen. Moreover, the ideal detergents should be left no residue after use which might harbour micro-organisms. Additionally, it should be non-toxic to pigs and must have minimal environmental impacts. Therefore, in order to avoid detergent residues and possible effects on pigs, staffs, and environment, the development of a novel technology without chemicals to kill micro-organisms, degrade atmospheric fine particulate matter and harmful gases in the pig farms is pioneering and future potential. In this study, application of degradation technology of light-emitting diode (LED) and auto-monitoring system for harmful substances in farrowing area of a specific pathogen-free pig farms. The LED light exhibit an antibacterial efficacy of up to 89.1%. They are capable of reducing ammonia concentrations within the environment by 45%, hydrogen sulfide levels by 68.8%, and curbing PM2.5 concentrations by 80%. Collectively, these findings underscore the considerable potential of this degradation technology via visual light in effectively eliminating detrimental substances.

Molecular characteristics and potential source of urban PM2.5-bound water-soluble organic matter in Shanghai during springtime

Water-soluble organic matter (WSOM) is complicated component in atmospheric fine particulate matter (PM2.5) and has attracted increasing attentions due to their effects on air quality, climate change, and human health. In this study, the molecular characteristics and potential source of PM2.5-bound water-soluble organic matter in Shanghai during springtime were investigated using ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) coupled with electrospray ionization (ESI) source in negative ion mode. A total of 2537 monoisotopic molecular formulas were assigned in WSOM, and CHO, CHON, CHOS and CHNOS compounds were main components. Highly unsaturated CHO (DBE = 5.01−7.30) and CHON (DBE = 3.66−10.1) compounds were found in good weather days, while the highly oxygenated multifunctional CHOS compounds (O/C = 0.50−0.88) increased in slightly polluted days. Van Krevelen diagrams revealed that carbohydrates, lignin species, and protein species were the most abundant classes in all samples, which their relative abundance accounted for 81.8%. Comparison of our results with previously published data on atmospheric PM2.5 indicated that the biogenic aerosol and local industrial emission presented a larger impact on molecular compositions of WSOM in this region. These findings present a comprehensive overview of WSOM in Shanghai, which helps to understand their characteristics and sources.