Atmospheric Environment: X最新文献

India implemented a range of multifarious strategies to address the issue of substandard air quality. One such flagship scheme of government of India is National Clean Air Programme (NCAP), which recommends sector specific reduction in emissions and increase in forest cover etc. To reduce particulate matter concentrations by 40% in 2026 compared to 2019. The present study aims to gauge the impact of Land Use Land Cover (LULC) changes alone on success of NCAP, using weather research forecasting model with chemistry (WRF-Chem) and integrated geographical information system and remote sensing software Terrset. The findings elucidate that, by the year 2026, the Ventilation Coefficient (VC) in India's eastern, central, northern, and north-eastern regions is anticipated to register a decline ranging from 18% to 50% compared to the baseline year of 2019. Conversely, an increase of 17% is expected in the southern region. The alterations in Fallow Land, Barren and sparsely vegetated land, Urban and Built-up Land, and Tundra, contribute to these shifts, displaying varying percentage changes across distinct zones. Simulations indicate that these LULC changes are impeding the planned reduction in PM_2.5 levels. Projections suggest an increase in PM_2.5 levels as high as 13% in the eastern, central, northern, and north-eastern regions, accompanied by a decrease of 33% in the Southern zone of the country. Significantly, non-attainment cities in Himachal Pradesh and Maharashtra are expected to witness a substantial rise in PM_2.5-induced premature mortality, with Pune city projected to experience over 24,525 additional premature deaths by 2026. A comparable examination conducted for the year 2022, utilizing actual LULC data, suggests that if the NCAP fails to effectively implement LULC changes, it may reduce this anticipated trade-off. Addressing this concern, the study employed WRF-Chem to simulate 60 combinations, proposing LULC enhancements conducive to improving VC. The results underscore the critical importance of preserving at least 36% of the LULC category of mixed forest land, encompassing plantations, orchards, and areas under shifting agriculture. Additionally, a reduction in barren land and fallow land emerges as pivotal for enhancing the ventilation coefficient. The study accentuates the necessity of refraining from further expansion in densely populated areas to counter these anticipated VC trends. This study provides valuable insights, highlighting the need to prioritize LULC management to effectively combat the alarming air pollution.

Bioaerosols are biologically originated particles in the atmosphere, which is mainly composed of bacteria, fungi, viruses, pollen, spores, and the fragmentation and disintegration of plants and animals. Bioaerosols are easy to be spread in the lower atmosphere and cause various epidemic diseases, which is harmful to human health. The forecasting and alert of bioaerosols have important scientific significance and reality needs. In this paper, a method is proposed for estimating and predicting the concentration profile of atmospheric bioaerosols using fluorescence lidar observational data. Using the powerful nonlinear prediction ability of artificial neural networks and through repeated training, a mathematical model can be established for the relationship among atmospheric environment, meteorological parameters, and bioaerosol concentration profiles. The input parameters are temperature and humidity, aerosol extinction coefficient, backscatter coefficient, PM2.5, PM10, SO₂, NO₂, CO, O₃, and wind speed, and outputs the concentration profile of bioaerosols. The prediction results with the measurement relative deviation of genetic algorithm back propagation (GA-BP) neural network and adaptive genetic algorithm back propagation (AGA-BP) neural network were analyzed. The results indicate that the AGA-BP neural network can effectively predict the concentration distribution of bioaerosols, and the predicted concentrations of bioaerosols are 1793 particles × m⁻³, 3088 particles × m⁻³, 5261 particles × m⁻³, 7410 particles × m⁻³ and 9133 particles × m⁻³ for air quality with superior, fine, mild contamination, middle level pollution and heavy pollution at an altitude of 0.315 km, respectively. We found that the predicted concentration of pollution weather is much higher than that of good weather. Furthermore, the AGA-BP neural network was used to predict the concentration profiles of atmospheric bioaerosols under different weather conditions, which provided a new research method for forecasting and alert of atmospheric bioaerosols.

Urban ecosystem is an intricate agglomeration of human, fauna and flora populations coexisting in natural and artificial environments. As a city develops and expands over time; it may become unbalanced, affecting the quality of ecosystem and urban services and leading to environmental and health problems. Fine particulate matter (particulate matter with aerodynamic diameter ≤2.5 μm - PM_2.5) is the air pollutant posing the greatest risk to human health. Quito, the capital city of Ecuador, exhibits a high occurrence of exposure to unhealthy levels of PM_2.5 due to a combination of natural and social variables. This study focused on three central parks of this high elevation city, investigating the spatial distribution of PM_2.5 concentrations. The particle pollution was then modeled using Normalized Difference Vegetation Index (NDVI). Hazardous instantaneous levels of PM_2.5 were consistently found on the edges of the parks along busy avenues, which are also the most frequented areas. This raises concerns about both short- and long-term exposures to toxic traffic pollution in recreational areas within urban dwellings in the global south. The NDVI model successfully predicted the spatial concentrations of PM_2.5 in a smaller urban park, suggesting its potential application in other cities. However, further research is required to validate its effectiveness.

The increasingly pronounced compound pollution issue of fine particulate matter (PM_2.5) and surface ozone (O₃) concentrations in China has exacerbated the risk of human morbidity and death. In this study, the spatial and temporal characteristics, health risks and synergistic control pathways of PM_2.5–O₃ compound pollution in 365 cities in China from 2015 to 2020 were investigated based on spatial statistical analysis, integrated risk index model and spatial correlation analysis. The results show that: The strict air pollution control measures lead to a sustained decrease in PM_2.5 leading polluted cities and a sustained increase in clean cities during the study period. However, there is a trend of increasing (2015–2017) and then decreasing (2018–2020) in cities with compound PM_2.5 and O₃ pollution because of changes in volatile organic compounds (VOCs) and NOx caused by human activities. According to the exposure analysis method, the population exposed to PM_2.5 dominated polluted cities declined by 471 million from 2015 to 2020; in contrast, the population living in clean cities increased by 460 million. With the intensification of PM_2.5–O₃ compound pollution in China, the exposure to PM_2.5–O₃ compound pollution urban population increases sharply from 349 million in 2015 to 622.5 million in 2018, an increase of more than 40 %; as air quality improves after 2017, the population exposed to PM_2.5–O₃ compound pollution gradually decreases, falling to the equivalent level in 2015 by 2020. Meanwhile, the population health risks attributed to PM_2.5 pollution were reduced, whereas the population health risks attributed to PM_2.5–O₃ compound pollution were aggravated. From a spatial perspective, PM_2.5–O₃ compound pollution and health risk exacerbation regions were concentrated in northern and eastern China. In addition, we found that PM_2.5 and O₃ concentrations have significant synergistic trends, which are consistent with the spatial distribution of VOCs and NOx. Therefore, the establishment of a scientific early warning system for PM_2.5–O₃ compound pollution and the continuous and vigorous promotion of comprehensive emission reduction of NOx and VOCs are conducive to the synergistic management of PM_2.5 and O₃ in China.

Spatial distributions of interleukin-8 (IL-8)-based relative inflammation potentials (IP) of PM_2.5 from vehicle exhaust and non-exhaust emission sources in Japan are derived using the meteorology–chemistry model (NHM-Chem) and laboratory experiments. In this study, IP is first defined as multiplying PM_2.5 from different emission sectors by supernatant IL-8 concentrations released using PM_2.5 samples, normalized to that of particle-free controls. The simulated IP of primary exhaust particles IP(E) accounts for 3%–30% of the total vehicle IP (exhaust + non-exhaust, primary + secondary), IP(V), which is low in densely populated regions (3%–15%) and high (5%–30%) in less populated regions, because there are fewer exhaust PM_2.5 emitters (diesel trucks) in more populated regions. The contribution of IP(V) to IP of the total environmental PM_2.5, IP(A), varied substantially in space by approximately 3–5 times (the contributions are greater in larger cities as there is more traffic). In our estimates, IP(V) is approximately one and two orders of magnitude higher than IP(E) and IP(T), the IP of fresh tire wear particles (TWPs), respectively. IP(T) has a minor contribution to IP(V) and IP(A). Recently, however, aged TWPs have been reported to be toxic; thus, the aging process of TWPs needs to be considered in the future.

In this study, we conducted a thorough investigation into the critical volatile organic compounds (VOCs), namely benzene, toluene, and xylenes (BTX), originating from the aromatic production industry. Our primary goal was to assess their spatial dispersion and source contribution, providing a comprehensive evaluation of their environmental and health impacts. The aromatic plant's average annual benzene concentrations were found to be compliant with Thailand's standard. However, xylenes did not meet the mandatory standards and emerged as the dominant species in the surrounding vicinity, with both maximum hourly and average annual concentrations exceeding the limits. Emission rate, meteorological characteristics, and topographical levels were identified as key factors affecting pollutant dispersion. The study utilized the maximum incremental reactivity (MIR) method to evaluate environmental risk assessment by calculating the ozone formation potential (OFP) of BTX. The total OFPs in the environment contributed by the aromatic plant ranged from 2.64 to 18.75 μg/m³. Xylenes emerged as the primary contributor to OFP concentrations at all receptor sites, accounting for 93–95% of the total OFP due to its high concentration and reactivity, followed by benzene and toluene. Storage tanks and wastewater treatment systems were identified as the main sources of ozone formation for benzene, toluene, and xylenes. Health risk assessment indicates an acceptable chronic hazard quotient (HQ) for each target organ system. For cancer risk, benzene slightly exceeds 10–6 at all receptors, necessitating consideration of pollutant concentrations, exposure duration, and other factors. The study emphasizes the importance of a comprehensive ambient monitoring network and updated emission inventory for effective air pollution management for the petrochemical enterprise, particularly in industrial areas.

Understanding the spatiotemporal dynamics of aerosol optical characteristics is crucial for assessing their impact on the climate system. This study focuses on Aerosol Optical Depth (AOD) at 550 nm, measured by the Moderate-resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, over a decade (2011–2021) in ten major cities across Pakistan. Our primary objectives were to investigate AOD variability, assess its correlation with cloud parameters, examine the source and trajectory of aerosol-laden air masses, and analyze the relationship between AOD and the Angstrom exponent. We employed the Hybrid single-particle Lagrangian Integrated Trajectory (HYSPLIT) model to trace air mass origins and paths. AOD exhibited its highest values in low-latitude urban areas, reflecting significant human activity. Conversely, high-altitude and mountainous regions displayed the lowest AOD levels. In summer (June–August), AOD peaked at 1.19, while in winter (December–February), it dropped to 0.24. The negative correlation between AOD and the Angstrom exponent, particularly in southern and western Pakistan, highlighted aerosol particle size variations. We further explored the relationships between AOD and five cloud parameters: water vapor (WV), cloud fraction (CF), cloud optical thickness (COT), cloud top temperature (CTT), and cloud top pressure (CTP). These relationships were found to be weather-dependent. This study provides valuable insights into the spatio-temporal dynamics of AOD in Pakistan, contributing to a better understanding of its impact on climate. This information is essential for climate scientists, meteorologists, and environmental departments, facilitating informed decision-making and climate modeling in the region.

Mobile air pollution measurements have the potential to provide a wide range of insights into emission sources and air pollution exposure. The analysis of mobile data is, however, highly challenging. In this work we develop a new regression-based framework for the analysis of mobile data with the aim of improving the potential to draw inferences from such measurements. A quantile regression approach is adopted to provide new insight into the distribution of NO_x and CO emissions in Central and Outer London. We quantify the emissions intensity of NO_x and CO (ΔNO_x/ΔCO₂ and ΔCO/ΔCO₂) at different quantile levels (τ) to demonstrate how transient high-emission events can be examined in parallel to the average emission characteristics. We observed a clear difference in the emissions behaviour between both locations. On average, the median (τ = 0.5) ΔNO_x/ΔCO₂ in Central London was 2x higher than Outer London, despite the stringent emission standards imposed throughout the Ultra Low Emissions Zone. A comprehensive vehicle emission remote sensing data set (n ≈ 700,000) is used to put the results into context, providing evidence of vehicle behaviour which is indicative of poorly controlled emissions, equivalent to high-emitting classes of older vehicles. Our analysis suggests the coupling of a diesel-dominated fleet with persistently congested conditions, under which the operation of emissions after-treatment technology is non-optimal, leads to increased NO_x emissions.

Nowadays, exposure modelling has become the preferred method for assessing human air pollution exposure due to its capability to predict air pollution under various conditions. The land use regression model (LUR) is a widely conducted model utilized to estimate air pollutants especially in unmonitored locations. However, the application of the model is still lacking in developing countries, especially in the Southeast Asia region. Therefore, this study was conducted to develop the LUR model to estimate PM₁₀ and NO₂ concentrations in Peninsular Malaysia. Multiple linear regression with a supervised forward stepwise was used to develop the models, and the models were validated using the leave-out-one cross-validation (LOOCV) approach. Results showed that the LUR model of PM₁₀ explained 58.5% variation, while the NO₂ LUR model described 86.8% variation. The difference value of PM₁₀ model R² and LOOCV R² were between 0.1% and 1.2 %, and the NO₂ models were between 0.01% and 0.08% depicting the robust stability of the models. Both models indicated that increased road and industrial areas significantly influence PM₁₀ and NO₂ concentrations. Nevertheless, more studies on the LUR model should be conducted in developing countries to assess the model's applicability in the region.

The petrochemical industry is one of the main sources of industrial volatile organic compounds (VOCs) emissions. In this study, typical petrochemical refining enterprises in Southeast China were selected, direct testing of VOCs in 18 petrochemical processes, and 87 samples were obtained using different on-site sampling methods, such as stack, fugitive, static and dynamic sealing point emissions sampling methods, based on the key process units, tank areas, loading and unloading areas, and plant boundaries of the petrochemical industry. Simultaneously, on-site concentration testing and laboratory analysis of 115 VOCs were conducted. Our findings reveal that, although the overall industry emission profile predominantly consists of low-carbon alkanes and alkenes, with relatively minimal halogenated hydrocarbon VOC emissions, there are substantial discrepancies in the primary species across different stages. The mass percentages of alkanes, alkenes, aromatics, halogenated hydrocarbons, and oxygenated VOCs in different process units of the petrochemical industry were 55 ± 27%, 8.5 ± 15%, 23 ± 27%, 3.9 ± 4.3%, and 10 ± 8.4%, respectively. The dominant species in the atmospheric vents of the depropanizer, light hydrocarbon recovery unit, continuous reforming unit, catalytic cracking unit, and sulfur recovery unit were n-butane (15%), n-hexane (13%), propane (21%), propylene (26%), and ethylene (28%), respectively. The dominant species in the gasoline tank top source profile was isopentane (48%), while that of the gasoline loading and unloading area was methyl tert-butyl ether (19%). High-carbon alkanes such as n-decane, n-octane, and n-heptane (>5% mass fractions) were prominent in kerosene tank tops. Furthermore, the results of the chemical reactivity assessment indicate that VOC emissions during the loading and unloading processes, as well as the ethylene production process, should be managed to mitigate ozone formation potential. According to the cancer risk assessments, benzene was the main factor that increased the risk, and its levels were far beyond the accepted cutoff point.