Atmospheric Environment: X最新文献

The emissions from the civil aviation sector are a significant source of CO₂ and air pollutants, which represent a serious threat to ambient air quality and public health. To gain a deeper understanding of civil aviation airport emissions, it is imperative to develop a precise and comprehensive emission inventory of China's civil aviation airports. However, there are limited studies dedicated to analyzing and verifying the accuracy and completeness of China's civil aviation emission inventory. Here, this study explored pollution characteristics from temporal trends and spatial distribution perspectives based on a previously developed 2019–2020 high-resolution air pollution and CO₂ emission inventory of the landing and take-off (LTO) cycle of civil aviation airports in China and the ChinaHighAirPollutants (CHAP) dataset. Besides, this study established an empirical model to evaluate the relationship between the air pollutant emissions of China's civil aviation sector in 2019–2020 and the pollutant concentration from the CHAP dataset. Compared to those in 2019, the total NOx, CO, PM, and SO₂ emissions during the LTO phase in China's civil aviation sector in 2020 decreased by 14.29%–24.32%, and the average concentrations of NO₂, CO, PM₁₀, and SO₂ in 2020 decreased by 6.33%–9.45%. The eastern, central, and southern regions of China are characterized by high emissions of pollutants, a phenomenon closely related to the economic prosperity and tourism development in these areas. They tend to boast higher route densities, increasing air transport activity and consequently resulting in elevated emissions. In addition, NOx had the highest correlation coefficient in the empirical model, with a correlation coefficient of 0.603 in 2019. Our findings provide new insights into civil aviation emissions in China from the analysis of the emission inventory of air pollutants and the CHAP dataset and provide a new method for verifying the accuracy and completeness of China's civil aviation emission inventory.

Elevated concentrations of particulate matter (PM) significantly deteriorate the air quality; however, the contributions from regional versus remote anthropogenic sources have remained uncertain over the western Indian region. In this regard, we have performed high-resolution regional modeling (WRF-Chem v3.9.1) to quantify the contribution of regional versus trans-regional anthropogenic sources to PM_2.5 (fine PM) and PM_2.5-10 (coarse PM) concentrations in contrasting seasons. Seasonal variability in spatial mean Aerosol Optical Depth (AOD) derived from the WRF-Chem model (0.21–0.42) agreed reasonably with MERRA-2 reanalysis (0.29–0.54) and MODIS satellite (0.23–0.51) over western India. Variability in surface PM_2.5 and PM₁₀ concentrations were also reproduced as per the benchmarks (|Fractional Bias| ≤ 60% and |Fractional Error| ≤ 75%) at most of the stations in this region. Results from sensitivity simulations reveal the dominant contribution of both regional and trans-regional anthropogenic sources to PM_2.5 concentrations over western India in winter and post-monsoon, when PM_2.5 concentrations are generally high. On the other hand, contribution from background levels (due to domain-wide natural emissions, fire emissions and pollutant transport from beyond domain boundaries) is highest during pre-monsoon and monsoon with a significant contribution of mineral dust especially to PM_2.5-10 (coarse PM). Analysis of PM spatial distribution at ∼900hpa pressure level reveals greater relative contributions of trans-regional emissions and background levels compared to that near the surface. Our study highlights key roles of trans-regional anthropogenic emissions and mineral dust, besides the local and regional emissions, in air pollution over western India. The quantitative analyses presented here would be useful for designing measures to minimize health and environmental impacts in line with the objectives of the National Clean Air Programme (NCAP) in India.

Over recent decades, the adverse impacts of airborne particles on human health have received wide attention. Elevated PM concentrations on underground platforms might pose a significant public health issue within underground metro systems. This study explores the impact of introducing a new type of train on the concentration of airborne particles on an underground metro platform through statistical modelling, analyses interactions between various factors, and estimates air quality on underground platforms after introducing a new type of train. Based on the data from a long-term field measurement, a linear mixed model, the multi-factor interaction model, which is an expansion of a previous multi-factor model, explored the impacts of train operations, passenger flow, urban background PM levels, ventilation, nighttime maintenance work, and their interactions on hourly PM10, PM2.5, and PM1 values on the platform. The model results show a positive correlation between those factors and platform PM10, PM2.5 and PM1 values, with significant interactions among these factors. The new model has a higher estimate quality than the previous model. Based on the combination of the model and measurement results, the levels of underground PM decreased significantly after replacing the old type of trains with new ones.

Aviation contributes about 4 % of global anthropogenic climate forcing primarily by contrails, CO₂ and NO_x emissions. Renewably sourced aviation kerosene can help to reduce the climate impact from CO₂ and from contrails, but so far, its production capacities are very small. Hence, the climate impact of using fossil fuel-based kerosene with a hydrogen content increased by hydroprocessing as short term mitigation measure is studied here. Therefore, the change in net energy forcing (ΔEF_net) in 2019 is calculated as the sum of the change in contrail energy forcing (ΔEF_contrail) and additional CO₂ emissions (ΔEF_{hydroprocessing}) from aviation kerosene hydroprocessing (ΔEF_net = ΔEF_contrail + ΔEF_{hydroprocessing}). The results show that hydroprocessed aviation kerosene can reduce the net energy forcing EF_net by about 33 % with ΔEF_{hydroprocessing} penalty of 5 %-points. Increasing the hydroprocessing severity increases the relative climate benefit, which is only slightly affected by the emissions factor for hydroprocessing or the choice of the time horizon. Data limitations about fuel composition and its effect on contrails and climate cause considerable uncertainties and the fuel's compliance with specification standards needs consideration. This study on the climate effect of hydroprocessed fossil kerosene can help to assess near-term measures to reduce the climate impact from aviation.

Emissions from agriculture are a worldwide problem as it is the major anthropogenic source of ammonia, methane, and nitrous oxide. Several efforts have been made to mitigate emissions. To achieve this, reliable measuring techniques are necessary to quantify the impact of the emissions. Different techniques relying on different principles are available. Generally, these techniques demonstrate good agreement on their measurements but there is a lack of studies that thoroughly investigate cross-interferences. In this work, three different models of Cavity Ring-Down Spectrometers measuring ammonia, nitrous oxide, and methane were tested in parallel for potential biases due to interference from ammonia, water vapor, and twelve volatile organic compounds commonly present in agricultural environments. Our results showed a small negative bias with increasing humidity on nitrous oxide and minor interferences of ammonia on nitrous oxide and methane. None of the tested volatile organic compounds interfered with ammonia, methane, or nitrous oxide measurements. Overall, concentration measurements of ammonia, nitrous oxide, and methane with cavity ring-down spectrometry have proven reliable under typical agricultural conditions. Minor interferences were only observed under exceptional conditions.

This study provides an extensive analysis of the spatio-temporal association between particulate matter of 2.5 μm or less (PM_2.5) and ground-level Ozone (O₃) across four selected urban settlements (Delhi, Bengaluru, Ahmedabad, and Kolkata), and a rural (Gadanki) area in India. Utilizing 4 years (2019–2022) data from multiple sites in India, the study employed the robust linear regression, and deweathering techniques to elucidate the dynamics of PM_2.5 and O₃ under varying environmental conditions. Key findings include, in urban areas like Kolkata and Bengaluru, PM_2.5 and O₃ exhibited a consistent year-round positive relationship pre- and post-deweathering. This implies that within these cities, emission sources, and atmospheric chemistry are crucial in shaping the association between PM_2.5, and O₃ than meteorological conditions. In contrast, negative correlations were more dominant over Delhi and Ahmedabad, which were unaffected by meteorology except in a few seasons. Typically, in Ahmedabad, this relationship differed from the general trend, displaying a positive correlation in winter and a negative in the pre-monsoon season. The rural area of Gadanki presents a unique case where deweathering alters the observed correlations significantly (shifted from positive to negative association), highlighting the dominant role of meteorological factors in driving PM_2.5 and O₃ relationship in rural settings. Relative humidity (RH), temperature (T), and wind direction (WD) were the key factors influencing PM_2.5 and O₃ relationship, although their impact varied seasonally and by location. However, the analysis during COVID-19 lockdown highlights the combined impact of meteorology and anthropogenic emissions on PM_2.5 and O₃ association, rather than the effect of each factor individually. These outcomes emphasize the need to account for both meteorological and non-meteorological factors in the air quality analysis. The findings offer valuable insights into coordinating the control of these pollutants, suggesting that effective air quality control strategies should be tailored to the specific needs and conditions of each region. This approach is crucial for developing more effective and targeted air quality management policies, especially in a diverse and rapidly developing country like India.

Exhaust gas purification is required for the operation of heavy machinery, e.g., construction machinery which mainly uses diesel engines. Precious metals such as the platinum group are used in catalysts for this purpose, which heavily impacts the environment. In this study, the authors evaluated the potential of remanufacturing diesel particulate filters (DPF) to reduce these impacts. Climate change indicators, i.e., global warming potential (GWP), and resource consumption were evaluated.

As a result, the environmental impacts of new product manufacturing, particularly resource production and the manufacturing process, were quantitatively estimated to be significant, while the environmental impacts of the remanufacturing process, product delivery, and disposal of the used products were significantly lower. In addition, 47% of the GWP and 50% of the resource consumption were reduced using remanufactured diesel particulate filters compared with using only new diesel particulate filters.

Low-cost sensors (LCS) have the potential to provide accurate and reliable measurements of air quality in real-time. This improves our ability to monitor, identify sources of pollution and develop mitigation strategies for effective air quality management. However, recent research on LCS has primarily focused on monitoring, exposure assessment, and calibration. In this study, we investigate the applicability of LCS data collected at ambient sites for characterizing and apportioning aerosol sources. Non-negative matrix factorization (NMF) was applied to the size-resolved data collected across five sites within the Indian Institute of Technology Bombay (IITB) campus in Mumbai using the LCS Alphasense OPC-N2. The sampling was done for 15 days at 5 locations in IITB, and each site only had 3 days of data. NMF resolved two factors for three sites, namely aromas (S2), hostel hub (S3) and central library (S4), while three factors were resolved for two sites, namely construction site (S1) and main gate (S5). Two common sources were determined for all the sites: (i) dust and marine source and (ii) traffic and combustion sources, which agree with the sources identified by studies in the literature. The third factor resolved at sites S1 and S5 is representative of heavy-duty diesel vehicles (HDDVs), which is present for a very short period and is captured because of the capability of high temporal resolution of the LCS. This offers a unique, cost-effective advantage of LCS for capturing episodic activities. The study suggests that in low- and middle-income countries with limited air quality monitoring capabilities, the size-time-resolved PM concentration data obtained from a network of low-cost sensors can estimate the pollution sources. This study provided evidence that despite their inherent limitations, LCS can be useful in attaining interpretable information about pollution sources and recommends extensive use of LCS for source characterization in the future.

Aerosol size distributions near biomass-burning sources undergo rapid evolution, primarily due to coagulation, which significantly alters the particle number size distribution. Existing long-range aerosol transport and climate prediction models often overlook near-source dynamics involving simultaneous coagulation and dispersion. To bridge this gap, the present study introduces a coagulation-dispersion model and provides semi-analytical solutions for the effective size distribution parameters. The precise solution for a diffusion-less coagulating plume with spatially varying particle concentration supports the conceptual accuracy of the semi-analytical parameterization for dispersion-coagulation model. These solutions form the basis for a parameterization scheme that considers input parameters such as source dimensions, particle mass flux, particle size, and atmospheric conditions. Utilizing this parameterization for case-specific biomass burning emissions shows a decrease in number emission rate by approximately a factor of 600, while the count median diameter of the initial size distribution increases by around 7 times. Additionally, we estimate the optical properties of aerosols both before and after the introduction of the near-source parameterization scheme. Results indicate an increase by a factor of 4 in the aerosol extinction coefficient and by a factor of ∼20 in the scattering coefficient, which will significantly influence the calculation of aerosol optical properties in global models. These changes in optical properties primarily stem from modifications in aerosol size distribution resulting from near-source aerosol dynamics. The results are further discussed.

This study investigates the impact of meteorological variations on the long-term patterns of PM_2.5 in Delhi from 2007 to 2022 using the AirGAM 2022r1 model. Generalized Additive Modeling was employed to analyze meteorology-adjusted (removing the influence of inter-annual variations in meteorology) and unadjusted trends (trends without considering meteorology) while addressing auto-correlation. PM_2.5 levels showed a modest decline of 14 μg m⁻³ unadjusted and 18 μg m⁻³ meteorology-adjusted over the study period. Meteorological conditions and time factors significantly influenced trends. Temperature, wind speed, wind direction, humidity, boundary layer height, medium-height cloud cover, precipitation, and time variables including day-of-week, day-of-year, and overall time, were used as GAM model inputs. The model accounted for 55% of PM_2.5 variability (adjusted R-squared = 0.55). Day-of-week and medium-height cloud cover were non-significant, while other covariates were significant (p < 0.05), except for precipitation (p < 0.1). Wind speed (F-value: 98) showed the strongest correlation, followed by day-of-year (61), years (41.8), planetary boundary layer height (13.7), and temperature (13). Meteorological parameters exhibited significant long-term trends, except for temperature. Inter-annual meteorological variations minimally affected PM_2.5 trends. The model had a Pearson correlation of 0.72 with observed PM_2.5, underestimating episodic peaks due to long-range transport. Partial dependencies revealed a non-linear PM_2.5 relationship with meteorology. Break-point detection identified two potential breakpoints in PM_2.5 time series. The first, on October 1, 2010, saw a significant increase from 103.4 to 162.6 μg m⁻³, potentially due to long-range transport. Comparing meteorology-adjusted and unadjusted trends can aid policymakers in understanding pollution change causes.