Atmospheric Pollution Research最新文献

This study utilizes ten years of wintertime boundary layer meteorological and surface air quality observations to characterize the nocturnal boundary layer (NBL) stability and assess its relationship with air pollution in Taiyuan, a highly polluted basin city in China. An unsupervised learning feature extraction technique known as the self-organizing map (SOM) is applied to objectively classify nocturnal virtual potential temperature (VPT) profiles. The SOM-based classification scheme allows the representation of wintertime day-to-day NBL evolutions by just nine regimes. Special attention is given to four dominant regimes: weak to moderate stability regime (NBL1), cloudy moderate stability regime (NBL3), windy moderate stability regime (NBL7), and strong stability regime (NBL9). These dominant regimes have relatively higher occurrence frequencies (>10%), with the highest frequency associated with the strong stability regime (NBL9) at 25.2%. The diurnal cycles of selected pollutants (CO, NO₂, SO₂, and PM_2.5) exhibit significant distinctions among the different NBL regimes. For instance, in the strong stability regime, CO, NO₂, and PM_2.5 show explosive growth in the evening due to the accumulation of primary pollutants. However, in the cloudy moderate stability regime, PM_2.5 exhibits persistent slow growth throughout the day, likely due to secondary particle formation under high humidity and high SO₂ conditions. These findings enhance our understanding of NBL meteorological impacts on surface air pollution in basin cities.

Field observation analyses of volatile organic compounds (VOCs) have gained significant attention due to their potential explaining atmospheric temporal trends. This study measured non-methane hydrocarbons (NMHCs) in Wuxi City, located in the Yangtze River Delta (YRD) of China, in 2010 and 2020. The aim was to gain a better understanding of the ten-year change of VOCs in terms of their mixing ratios, sources, role in ozone, secondary organic aerosol (SOA) formation, and health risks. The average NMHC level 2020 was 20.67 ppbv, which indicated a 20% drop from 2010. A notable shift was observed in the chemical composition of NMHCs. The contribution of alkanes increased from 45% to 62% in 2020, while that of the aromatics decreased from 25% in 2010 to 21% in 2020. Ratio analysis and receptor modelling for 2010 and 2020 were used to identify the source changes of VOC species. The source apportionment model showed that the contribution of vehicle exhaust decreased from 34.5% to 20.9% from 2010 to 2020, and that of the solvent increased from 15.1% to 25.3%. The chemical reactivity results suggested that aromatics played significant roles in photochemical reactions in Wuxi City. The ozone formation potential (OFP) and SOA potential of VOCs also showed decreasing trends from 2010 to 2020. The total OFP of alkenes decreased by 34.0%, from 30.6 to 20.2 ppbvO₃/ppbv. The total non-carcinogenic risk value for the key VOC species was 0.034 in 2010 and 0.024 in 2020. Although the carcinogenic risks for benzene have decreased significantly, the value is still above the acceptable carcinogenic risk level. The results of this study demonstrate the effectiveness of air pollution control measures in Wuxi City and highlight the positive impacts on reducing secondary pollution and health risks in this region over one decade.

Shanghai experienced the COVID-19 lockdowns (LCD) in the winter of 2020 and the spring of 2022, respectively. In this paper, the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) modeling system was used to simulate ozone (O₃) in Shanghai, and the contributions of meteorological conditions and emission reductions to the increases of the ground-level O₃ concentrations in Shanghai during the two LCD periods were analyzed. The relationships between O₃ concentrations and meteorological factors were quantified by the multiple linear regression model (MLR). The results showed that the increases in the concentrations of daily maximum 8-h moving average O₃ (MDA8 O₃) were caused by both meteorological changes and emission reductions, but their relative contributions to O₃ concentrations were different in winter and spring. Unfavorable meteorological conditions and the emission reductions during the 2020 winter LCD period increased MDA8 O₃ by 2.2 μg/m³ (2.2%) and 2.8 μg/m³ (2.8%), respectively, relative to the same period in the previous year. On the other hand, the meteorological conditions during the 2022 spring LCD period increased MDA8 O₃ by 7.9 μg/m³ (6.6%), while the emission reductions only increased MDA8 O₃ by 1.1 μg/m³ (0.9%). The O₃ formation in Shanghai urban areas was under the VOC-limited conditions during two lockdowns in winter and spring. Therefore, the VOCs emission reductions were more effective for the control of O₃ in Shanghai urban areas. The results emphasized the complex relationship between emission reductions, meteorological conditions and short-term O₃ changes.

The interannual trends in fine particulate matter (PM_2.5) and black carbon (BC) concentrations at remote stations in central (Noto) and southwestern (Fukue) Japan were investigated using statistical trend analysis and the chemical transport model, Regional Air Quality Model 2 (RAQM2ver3). The concentrations of PM_2.5 and BC in Noto and Fukue exhibited seasonal variations and decreased from 2013 to 2020. Higher concentrations of PM_2.5 and BC were observed during the spring season (April) in both locations. The PM_2.5 concentrations peaked in Fukue approximately one month earlier than in Noto, while the BC concentration peak in Noto occurred one month earlier than that of PM_2.5. The total reductions in PM_2.5 concentrations in Noto and Fukue were 7.5 ± 5.0 and 3.8 ± 0.95 μg m⁻³, corresponding to reduction rates 1.1 ± 0.7 and 0.5 ± 0.95 μg m⁻³year⁻¹, respectively, compared to the concentrations in 2013. The total reduction in BC concentrations in Noto from 2013 to 2019 was estimated to be 0.13 ± 0.1 μg m⁻³, amounting to 0.018 ± 0.014 μg m⁻³year⁻¹ reduction rate. The results of the source–receptor relationships analysis suggest that the decreases in PM_2.5 and BC concentrations in Fukue and Noto were significantly influenced by reductions in amounts transported from central China (CCHN, 30–40°N), northern China (NCHN, >40°N) and Japan (JPN) after 2015/2016. The decrease in emissions from these three regions accounted for the observed reductions in particulate concentrations.

Given the significant health and environmental risks posed by atmospheric ${\mathrm{PM}}_{2.5}$ pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the ${\mathrm{PM}}_{2.5}$ prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast ${\mathrm{PM}}_{2.5}$ concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves $R^2$ by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric ${\mathrm{PM}}_{2.5}$ pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.

Rapid urbanization and industrialization in China have resulted in an increase of PM_2.5 concentrations. In this study, an interpretable multi-model stacking ensemble method (IMSEM) with top-of-the-atmosphere reflectance (TOAR) from the Himawari-8 satellite were used to acquire high-resolution PM_2.5 data in China. In contrast to the traditional approach whereby PM_2.5 is estimated with single models, using TOAR data, IMSEM outperformed single models in terms of several skill scores. The hourly average R² (RMSE) of 10-fold-cross validation reached 0.84 (9.52 μg/m³) in 2021 by IMSEM. The feature importance results of IMSEM showed the significant contributions of TOAR and meteorological variables. The PM_2.5 estimates of IMSEM were also fused with surface observations using interpolation for correction and optimization. When this was done for PM_2.5 concentrations in 2022, it was found that, among the four seasons, the fusion-based estimate of PM_2.5 concentration was highest in winter (49.94 μg/m³), followed by autumn (31.59 μg/m³) and spring (29.07 μg/m³), and lowest in summer (19.25 μg/m³).

Among alternative fuels, hydrogen (H₂) holds significant promise as both a fuel and an energy carrier. It is expected to become a key alternative fuel in the near future to meet stringent pollution standards. H₂ is used in internal combustion (IC) engines, gas turbines, and the aerospace industry due to its non-toxic and odorless nature, high calorific value (CV), and wide temperature range of combustibility. Additionally, it is a long-term renewable energy source that produces fewer pollutants. This study explores the impact of different H₂ ratios on combustion behavior, engine performance, and emission characteristics in a dual-fuel compression ignition (CI) diesel engine. The current research focuses on the effect of TB of ethanol, Jatropha methyl ester, and diesel with the induction of H₂ with different flow rates in diesel engine in dual fuel mode (DFM). The tests were performed at different engine loads, 25%, 50%, 75%, and 100% at a constant engine speed of 2500 rpm. H₂ is inducted at different flow rates like 2, 4, and 6 Litres per minute (lpm). Partially stabilized zirconia (PSZ) is used to coat the cylinder head, piston crown, and valves with a thickness of 0.5 mm. The combined effect of TB fuel and H₂ in a CI engine improves combustion and engine performance. At full load condition, brake thermal efficiency (BTE) is improved by 11.5% for TB fuel with H₂ at 6 lpm, and exhaust gas temperature (EGT) increased by 12.7%. The cylinder pressure (CP) and heat release rate (HRR) are increased by 7.5% and 10.6% at TB fuel with 6 lpm of H₂ induction. The utilization of H₂ as an alternative energy source has significant consequences for the future of green energy production.

The emission from solid fuel combustion for cooking and heating has a direct influence on air quality in rural areas, especially in cold season. In this study, the pollution characteristics and chemical compositions of total suspended particle (TSP) were comprehensively studied in a typical rural area during the Chinese Lunar New Year as a case study. In addition, the environmentally persistent free radicals (EPFRs) and oxidative potential (OP) in TSP were analyzed and compared before and after the festival (BF & AF) for better understanding the exposure risk of TSP. The average values of typical combustion products, such as carbonaceous components and PAHs, were at 2.5 times higher BF than those AF due to the impact of anthropogenic emission. The average EPFRs concentration BF was 2.78 ± 0.70 × 10¹⁵ spins/m³, whereas the concentration significantly decreased AF (1.07 ± 0.48 × 10¹⁵ spins/m³). The source analysis suggested that coal and biomass burning was the major source of EPFRs. The OP_v values were in the range of 0.85–10.1 nmol/min/m³ with an average of 3.74 nmol/min/m³, and the peak occurred at the eve of the festival. For OP_m, its mean value was up to 12.3 ± 7.8 pmol/min/μg and showed positive correlation with crustal elements (r values for Fe = 0.51 and Mn = 0.49, p < 0.05), indicating the effect of dust-input. In summary, this study provided basic information for better understanding the pollution characteristic and health risk of atmospheric particles.

In this study, we investigated the aerosol radiative forcing (ARF) using ground-based measurements of PM_2.5 and black carbon aerosols at a semi-arid, rain shadow location, Solapur in peninsular India. It is observed that aerosols caused a net cooling effect at top of the atmosphere (TOP) indicating that the aerosols reflect more solar radiation back to space than they absorb. At the surface, the aerosols caused a net cooling effect indicating more presence of scattering type aerosols. The resulting ARF of the aerosols was found to be ranging from +38 Wm^-2 in monsoon to +53 Wm^-2 in pre-monsoon indicating trapping of energy which resulted in a warming of the atmosphere. However, BC –only forcing indicated a significant warming effect at TOP as well as in the atmosphere which showed the potential of the absorbing carbonaceous aerosols. Overall, BC was responsible for 44% and 32% of the composite ARF, even though it formed only 7% and 2% of composite aerosol in the dry and wet periods, respectively. The warming impact of BC aerosols was also manifested in terms of their contribution to aerosol radiative forcing efficiency (ARFE) which was about four times more for BC-only than that for composite aerosols. More atmospheric heating rates were observed during dry periods for composite and BC-only aerosols than during wet period. These findings have important implications for aerosol-cloud-precipitation studies as well as the atmospheric thermodynamics and hydrological cycle over this semi-arid region where the total aerosol load is not significant and rainfall amount is scarce.

The beauty industry has become increasingly popular in recent years, leading to a surge in salon visits for various beauty treatments. However, public concerns were raised about the possible health dangers linked to the widespread use of harmful chemicals in beauty salons. To address these concerns, a pilot study was conducted in Kuwait to evaluate and identify the ten most commonly occurring hazardous airborne chemicals in different beauty salons. Air samples were gathered from inside and outside ten beauty salons situated in diverse regions of Kuwait, covering various periods, including working hours, non-working hours, and a continuous 24-h period. The study used diffusion tubes for sampling and employed the thermo-desorption gas chromatograph/mass spectroscopy technique (TD-GC/MS) to identify the chemical compounds. In addition, the measured concentration of compounds was utilized to assess the potential inhalation health risks associated with both carcinogenic and non-carcinogenic substances, employing the USEPA Cancer Risk Indicator (CRI) and Hazard Quotient Indicator (HQI). The findings indicated that indoor concentrations of hazardous chemicals were significantly higher than those recorded outdoors. A total of 55 hazardous chemicals were detected in the indoor air quality inside beauty salons, and benzene concentrations exceeded ACGIH thresholds, suggesting a potential health concern. Although concentrations of toluene, ethylbenzene, m/p-xylene, and o-xylene remained below NIOSH recommendations, health risk assessments indicated the potential carcinogenicity of three compounds in the indoor air of beauty salons: benzene, ethylbenzene, and 2-ethyl-1-hexanol. Additionally, 15 compounds were identified by non-carcinogenic risk assessments, such as ethyl acetate and 2-ethyl-1-hexanol. These findings emphasize the urgency of implementing measures to address the exposure in Kuwait's beauty salons and conducting further research.