Atmospheric Environment: X最新文献

Neighborhood-scale air pollution hotspots have recently been identified through detailed field campaigns, including the 100x100 Black Carbon Experiment which took place in West Oakland, CA, in 2017. Here, high-resolution nested atmospheric simulations are used together with a Bayesian inversion framework to estimate source apportionment at the hyper-local scale for a neighborhood in West Oakland. Forward simulations are performed with the Weather Research and Forecasting (WRF) model using 6 grid nests from 11.25 km to 2 m horizontal resolution. On the finest grid, building geometries are resolved using the immersed boundary method. Seven point sources and four line sources at known locations are included in the forward simulation for two 1-h periods during the 2017 field campaign. Data from 12 black carbon sensors are used to perform source inversion using a Markov Chain Monte Carlo approach, which provides a probability distribution for each of the 11 source strengths. From this, a most-likely plume can be created using the peaks of the distributions, and source apportionment can be estimated for each sensor. In addition, a composite plume can be constructed to indicate 90% confidence that concentrations are above or below a specified value. With this probabilistic analysis, it is possible to determine that more than half of the neighborhood has black carbon concentrations of higher than 0.4 μg/m³, with some areas higher than 3 μg/m³ during the time periods studied.

Transportation is a major sector of anthropogenic emissions in urban areas and deteriorates air quality. The surface and vertical observational data were combined with the model results to reveal its impact on the horizontal and vertical variations of pollutants during the COVID-19 lockdown period. The evident reductions in ambient PM_2.5 (∼30%) and NO₂ (∼50%) concentrations but a ∼25% increase in O₃ concentration were observed at the transportation sites. On the vertical scale, a uniform decrease of ∼28% in PM_2.5 concentrations was observed within 600 m. However, the vertical profiles of NO₂ and O₃ exhibited increasing vertical variation rates with concentrations varying significantly within 400 m. Meanwhile, O_x shared a similar pattern of vertical profile with O₃, with a uniform increase (∼5%) within 600 m in the urban area. The WRF-CMAQ model reproduced the variations, and revealed that the reduction of transportation emissions was the key factor contributing to the increase of urban O₃ and O_x due to the weakened NO titration effect. The simulated vertical profile of NO₂ was featured by a decreasing curve, while that of O₃ exhibited the opposite trend. We find that the transportation emissions impact vertical concentrations of NO₂ and O₃ within at most 400 m. The process analysis revealed that the vertical transport and horizontal transport from bay areas contributed to O₃ in the urban area, while chemical processes mainly consumed it. The reduction in transportation emissions weakened the consumption and resulted in O₃ accumulation during rush hours and at night. The variation of planetary boundary layer height also favored the rise of urban O₃ by promoting vertical transport at daytime and trapping it at night. The reduction in NO_x emissions from the transportation enhanced O₃ pollution, suggesting that collaborative reductions in VOCs from multiple sectors should be conducted. This study also indicated that regional collaborations in emission reductions were necessary for comprehensive air pollution prevention.

India is experiencing a rapid urban growth in recent decades modifying the regional air quality around urban agglomerations. Hyderabad, the capital city of Telangana state in India, has been experiencing significant urbanization of about 17 % growth in urban agglomeration over the past two decades. We investigated the long-term pollution characteristics along with the meteorology in and around Hyderabad (300 km × 300 km) using satellite-based remote sensing, and reanalysis data. Columnar aerosol loading was highest during the Spring while the positive trend was more during the Winter. The northeastern and southeastern parts of the study domain experienced higher aerosol loading. A significant increasing linear trend in AOD and PM_2.5 is observed over the urban region as well as the northern and eastern parts. The NO₂ and SO₂ columnar concentrations showed considerable enhancement over the northeast sub-region where numerous thermal power plants are located, and over the urban centre. The SO₂ concentration and SSA values were higher during the Autumn, while the NO₂ values peaked along with lower SSA values during the Spring. The observed spatio-temporal features in air pollutants are further investigated using rainfall information, transport pathways, vegetation index, and fire events. Higher surface temperature and the polluted northeasterlies caused the comparative enhancement of NO₂ concentration during Spring. The investigation on the NDVI and the fire events in different sub-regions points to the possibility of enhanced human settlement, and thereby the associated anthropogenic activities are notable over the West and South parts of Hyderabad. However, the presence of thermal power plants in the northeast and natural gas plants along the coast act as persistent regional sources for aerosols and pollutant gases irrespective of the wet removal.

The observational characterization of anthropogenic methane (CH₄) emissions in the Eastern Mediterranean and Middle East (EMME) region, known for its significant oil and gas (OG) production, remains limited. Light alkanes, such as ethane (C₂H₆), are co-emitted with CH₄ by OG activities and are promising tracers for identifying the CH₄ emissions from this sector at the wider regional scale. In this study, in-situ measurements of CH₄ and alkanes (C2–C8 were collected during a field campaign at a regional background site (Cape Greco, Cyprus). A mobile laboratory housed the instrumentation at the south-eastern edge of the island between December 2021 and February 2022. This specific location and time of year were selected to capture air masses originating from distant southern and eastern regions, primarily impacted by sources from the Middle East. Based on these observations we 1) evaluate the significance of long-range transported versus local sources in Cyprus, 2) identify and document regional anthropogenic CH₄ sources with the help of the concomitant alkane measurements, and 3) assess the accuracy of the EDGAR sectoral emission inventory over the EMME region. The highest alkane mixing ratios observed were associated with the Middle Eastern OG CH₄ signal. Surprisingly, the Middle Eastern emissions of CH₄ were found to be heavily influenced by the breeding and waste management sectors. By investigating the measured CH₄ mixing ratios together with an atmospheric dispersion model (FLEXPART), we derive a comprehensive characterization of the pollution sources at a regional scale over the Eastern Mediterranean region. Our results indicate that CH₄ emissions from the Middle Eastern OG sector are likely underestimated by ca. 69 %. These findings underscore the efficacy of using experimental observations of alkanes for CH₄ source identification at receptor sites. This tracer approach would also benefit from a substantial revision of light hydrocarbon emission inventories.

In this paper we set up a modeling chain to study the impact of different urban planning scenarios on air quality and ultimately the exposure of the population. The analysis relates to the intensity of the polluting activities associated with each scenario, as well as their environmental and health impact. The implementation of a 2030 prospective scenario on Ile-de-France allows us to assess the magnitude of the leverage effect of the actions recommended in the regional master plan. The objective is to quantify the importance of emission reductions, but also the gain in terms of exposure to pollutants, which can be obtained when we transcribe into the model the implementation of regulatory texts on the metropolis of Greater Paris. The results allow us to debate the paradox between reducing emissions and increasing the exposure created by situations of high urban densification.

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.