Atmospheric Environment: X最新文献

Because of its relatively long lifetime among short-lived climate forcers in the atmosphere, carbon monoxide (CO) is utilized as a tracer, and is expected to be simulated at coarse resolution. To better grasp the behavior of CO in the atmosphere, multi-altitude measurement is required because the main sources of CO emissions are automobiles (surface) and industry (aloft). In this work, using CO measurements obtained at remote sites and through a tower measurement network in Japan (37 m and 250 m above ground level (AGL) in an urban area, and 32 m AGL at a rural site in the greater Tokyo area), the performances of a global model (2° × 2.5°) and a regional model at various resolutions (12, 4, and 1.3 km) were comprehensively evaluated. The global model successfully simulated CO at remote sites but not for high-concentration peaks at rural and urban sites, whereas the regional model increasingly improved its performance in capturing CO peaks at urban sites with resolutions up to 4 km. Therefore, we concluded that a 4 km resolution was suitable for capturing CO at urban sites, and furthermore estimated the source contributions. The regions surrounding the greater Tokyo area were dominated by the concentration from the lateral boundaries (approximately 180 ppbv), while the higher CO in central Tokyo was attributed to local sources. These local sources accounted for up to 80% of the annual average at the surface level and just 10% aloft (corresponding to the 250 m AGL site). Sensitivity simulations assessing CO sources (automobiles, industry, and others) demonstrated the important role of automobiles, while higher altitudes had more sources attributed to industry. Local sources were found to make more prominent contributions at higher concentration ranges. The appropriate modeling resolution for CO behavior can be drawn from our findings and the usefulness of simultaneous measurements at the surface level and using a tower for capturing the three-dimensional CO structure can be demonstrated as an important approach.

In India, scarcity of ground-based measurements of nitrogen dioxide (NO₂) is a major challenge for estimating long-term exposure and associated health impacts. This study aimed to develop and validate a national-scale annual NO₂ exposure model for India for 2019 and determine if model cross-validation predictive ability was improved by including non-continuous (manual) measurements along with reference-grade, continuous measurements.

We used a supervised forward-addition linear regression method to fit land use regression (LUR) models developed with up to 804 Central Pollution Control Board ground monitoring stations (n = 157 continuous, n = 647 manual) and 209 spatial predictor variables, including satellite-based tropospheric NO₂ columns. Two models were developed: one using continuous sites only and one using continuous and manual sites, with standard diagnostics and cross-validation (CV) methods. We also assessed if the kriging of final model residuals reduced spatial autocorrelation and improved model CV results. LUR coefficients for the best-performing model were applied to predictors for 2015–2021 and gridded at 100 m to estimate population-weighted exposure.

The continuous sites-only model and combined continuous and manual sites models had CV-R² values of 0.59 (root-mean-square error [RMSE]: 9.4 μg/m³) and 0.54 (RMSE: 8.3 μg/m³), respectively, and both included the satellite NO₂ predictor. Kriging residuals increased the CV-R² of the combined model to 0.70 (RMSE: 7.2 μg/m³) but offered no improvement for the continuous site model. National population-weighted average NO₂ was 22.1 μg/m³ in 2019. We estimated over 92% of the Indian population was exposed to annual NO₂ exceeding the WHO air quality guideline (10 μg/m³). In Delhi, Mumbai, and Kolkata, an estimated 45%, 100%, and 100% of the population, respectively, experienced annual NO₂ levels that surpassed Indian standards (40 μg/m³). To our knowledge, this is the first such long-term NO₂ LUR model specific to India, and predictions are available to interested researchers.

A key challenge in controlling deteriorating urban air quality is a lack of clear understanding of the regional emissions characteristics and their impact on human health. COVID-19 lockdown provided an opportunity to enhance understanding of background air quality. Towards this, we studied the effect of the lockdown on air pollutants level and associated health benefits in two contrasting urban cities of Eastern IGP, Asansol (industrial) and Kolkata (metropolitan), by analyzing data from 2019 to 2021. The outcomes revealed that the level of exceedance of air pollutants is usually higher in Asansol but significantly decreased in both cities during the lockdown period. Particle concentrations were reduced by 50–70 % compared to Pre-Lockdown and by 20–35 % against the same period in 2019. Kolkata witnessed a higher reduction in PM levels than Asansol. Diurnal variation comparison showed a higher reduction of particle levels during lockdown in the morning at Asansol while in the evening at Kolkata. The health benefits associated with the reduction in PM_2.5 concentration were quantified using the BenMAP-CE model, which revealed that improving air quality, like during the lockdown period, would save annually 0.46 and 2.91 deaths per 100,000 persons in Asansol and Kolkata, respectively. Altogether, this study's outcomes provide essential insights to policymakers for regional factors associated to varying air quality and health benefits associated to improvement in air quality.

Liquefied petroleum gas (LPG) is represented in the literature as a transport fuel with lower regulated and non-regulated emissions compared to petrol. For the purpose of this paper, real-world emissions measurements were performed on a random sample of four commonly operated passenger vehicles with higher mileage (over 200 thousand km) and on a new one. The aim is to compare the emission factors of particle-bound polycyclic aromatic hydrocarbons (PAHs) in dual-fuelled passenger cars running on LPG and petrol, or rather to compare the emission factors of the two fuel types. PAH concentrations are determined by gas chromatography based on the analysis of particles captured on a filter during isokinetic sampling of the exhaust gas during driving in real traffic. In terms of the sum of the 8 most hazardous PAHs analysed, LPG fuelling reduced the total (summary) emission factor of only two of the vehicles analysed, by 22% and 26% respectively compared to petrol. For the remaining vehicles, there was an increase in the total emission factor ranging from 4.4 to 68.8%. The results show that in terms of particle-bound PAHs, the benefit of LPG fuel in terms of vehicle emissions may not be so clear-cut in real-world tests and it depends very much on whether a suitable propulsion system is fitted to the vehicle, how it is set up and how the vehicle is serviced (i.e. the vehicle overall condition).

Scooters are a popular means of transportation in urban areas. However, studies examining their spatial and temporal features are lacking. This study examined traffic patterns in New Taipei City from September 2021 to June 2023 using real-time surveillance camera images and the YOLOv4 algorithm. The focus of the study was to investigate the role of scooters in urban transportation and their effect on air quality. The findings revealed that sedans and scooters accounted for approximately 90% of the total vehicles, and their usage exhibited significant spatial and temporal variations across the city. Sedans were more prevalent in rural areas, whereas scooters were predominant in urban and suburban regions. An examination of diurnal patterns revealed that peak traffic occurred during early morning and evening rush hours, with distinct usage patterns between weekdays and weekends. Through hierarchical clustering, the city's stations were categorized into three types based on the dominant vehicle usage: sedan-dominant, sedan-prevailing, and scooter-dominant. The analysis also established a correlation between the number of vehicles and air pollution, highlighting the significant role of sedans and scooters as primary sources of emissions, particularly in areas where scooters were dominant. This suggests that the current emission inventories may underestimate the impact of scooters and buses on air quality while overestimating the trucks' contribution. Consequently, this study emphasizes the significance of scooters in determining urban air quality and advocates for improved monitoring and image identification technologies to accurately assess the numbers and speeds of scooters. These measures are essential for improving the accuracy of emissions inventories and forecasts, which are crucial for effective urban air quality management.

Aviation contributes to anthropogenic climate change mainly by contrails, CO₂ and NO_x emissions, whereof contrails are considered the largest single contributor to the radiative forcing from aviation. Powering aircraft with kerosene containing fewer or no-aromatics, i.e., “Sustainable Aviation Fuels” (SAF) or hydroprocessed, fossil-based kerosene, can significantly reduce contrail climate forcing. However, such kerosene is currently scarcely available. Moreover, less than 10 % of the flights worldwide cause more than 80 % of the contrail climate forcing. Hence, this study investigates a targeted allocation of paraffinic, i.e., aromatics-free kerosene to flights and flight segments with the highest contrail climate forcing, by calculating the resulting contrail energy forcing (in J) on 844 364 flight trajectories worldwide departing from five large European airports in 2019.

The contrail radiative forcing integrated over contrail evolution (i.e., contrail energy forcing [J]) is simulated for a reference fleet powered with conventional kerosene of 14.1 m - % hydrogen content. 5 % of overall kerosene demand assumed to be paraffinic kerosene with 15.3 m - % hydrogen content is allocated via a uniform, a flight-specific and a segment-specific approach. The uniform allocation assumes that all flights receive the same blend of 5 % paraffinic kerosene. The other cases target 100 % paraffinic kerosene either to flights or segments with highest contrail energy forcing. Compared to the reference, the results indicate a reduction on contrail energy forcing by 4 %, 36 % and 55 %, respectively. For market shares of paraffinic kerosene up to 30 %, a segment specific allocation appears advantageous compared to a flight specific allocation. However, they might require airport and aircraft modifications. Uncertainties in contrail climate benefits can be reduced by providing additional information on kerosene properties and accurate meteorological data. Overall, this study highlights robust potentials of paraffinic kerosene to significantly reduce the climate forcing from aviation.

South Africa remains an uncharted realm in terms of marine mobile emissions inventory and hence the impact of pollution from ships in coastal cities remains unknown. Such a void creates uncertainties about the extent of population exposure to pollution from ships, the health impact and economic value associated with the changes in policies for the port city of Durban.

This study was aimed at estimating the health and economic impact of marine mobile emissions and the health and economic benefits associated with improvement in air quality within the port city of Durban, which is under eThekwini municipality. The population exposure to particulate matter of less than 2.5 and 10 μm (PM_2.5 and PM₁₀) and sulphur dioxide (SO₂) from ships calling into Durban port were estimated using the AERMOD air dispersion model. The BenMAP modelling tool was then used to estimate the health impact and economic value of changes in emission of PM_2.5, PM₁₀ and SO₂ from ships visiting Durban port. The reduction in emissions from ships was due to the reduction in the sulphur content of fuel from 3.5% to 0.5% that was implemented by the International Maritime Organisation (IMO) on the 01^st of January 2020.

The results showed that PM_2.5 and PM₁₀ emissions reduced by 63% in 2020 compared to 2018, whilst SO₂ emissions reduced by 82% over the same period. The BenMAP results indicated that 49 premature mortalities were avoided in 2020 compared to 2018 since the IMO's forced reduction in sulphur content of fuel by 85% in 2020. These avoided cases on mortality were estimated a monetary value of R228 million using the World Bank’s 2016 estimates and R683 million using the USEPA Mean VSL estimate. Such monetary values were respectively equivalent to 0.05% and 0.2% of the eThekwini's GDP, which was estimated at R468 billion in 2016.

In the backdrop of persistent haze occurrences affecting Southeast Asia and Pakistan's environmental landscape, this study delves into an in-depth analysis of atmospheric Particulate Matter (PM2.5) during intense haze episodes prevalent in Lahore throughout October, November, and December 2019. Employing advanced analytical techniques encompassing Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive Spectroscopy (EDX), X-ray Diffraction (XRD), and Raman Spectroscopy (RS), this investigation meticulously scrutinized PM2.5 samples. The findings showcased substantial variability in PM2.5 concentrations, peaking notably in December within the range of 43.2–301 μgm⁻³, averaging 168 ± 88.3 μgm⁻³, whereas lower concentrations ranging from 30.9 to 268 μgm⁻³, with an average of 106 ± 66.1 μgm⁻³, were observed in October. These concentrations displayed correlations with meteorological parameters, demonstrating a direct association with relative humidity and varying relationships with temperature and wind speed. The maximal PM2.5 concentrations aligned with lower temperatures (19.1 °C), while higher temperatures (26.1 °C) coincided with the lowest concentrations, illustrating distinct relationships with relative humidity percentages and wind speeds. Advanced spectroscopic analyses (RS and XRD) confirmed the presence of various minerals and elements within PM2.5 samples, encompassing calcite, calcium aluminosilicate, hematite, barite, quartz, gypsum, organic carbon, and nineteen elements identified by EDX. Morphological evaluations unveiled diverse particle shapes, from round, pointed, and irregular to rod-like, and agglomerate structures. SEM investigations delineated distinctive groups of anthropogenic and geogenic particles, emphasizing emission sources such as automobile emissions, crop residue burning, biomass burning, construction activities, soil dust, and industrial emissions. This comprehensive study lays the groundwork for source apportionment, vital for understanding consequential impacts on climate, visibility, and human health, fostering future investigations in this domain.

Bottom-up modelling is used frequently to estimate emissions produced by seagoing vessels, and the accuracy of modelling is dependent on the data the model is trained with. Observational studies can be used to increase the model accuracy. Here we compared data from two measuring campaigns conducted on board ships that use Liquefied Natural Gas (LNG) as primary fuel in internal combustion engines (ICE) in a diesel-electric setup with values obtained from the Ship Traffic Emission Assessment Model (STEAM).

The power demand for propulsion calculated using Automatic Identification System (AIS) data matched observations reasonably. The root mean square error between the modelled and observed power demand was 759–914 kW (28.6–34.5%) for the measured ropax vessel and 1869–1916 kW (16.7–17.1%) for the large cruise vessel over four voyages while the ships were underway. The discrepancy is largely explained by the auxiliary power demand, which was 4 times higher on the large cruise vessel than the model prediction.

Using meteorological data to estimate the increase of resistance did not improve the goodness of fit between modelled and observed engine power demand. STEAM model's base-specific fuel consumption calculation method fits observed values reasonably when the engine load is over 50%, but ICEs used in constant speed mode have increased consumption at lower engine loads compared to variable speed ICEs.

The share of pilot fuel of total energy consumption was found to play a significant role in the emission factors for measured exhaust gas compounds. More accurate functions to model fuel consumption and emissions were derived using the observed data.