Atmospheric Environment: X最新文献

Benzene as one type of hazardous air pollutants (HAPs) is produced by industrial production processes and/or emitted during upset events caused by man-made or natural accidents. Although upset emissions of benzene can be a significant contributor to the total emission, it is still challenging to quantify. This study first develops a fast modeling framework using obstacle-resolving computational fluid dynamics modeling to compare the modeled within-facility-scale passive pollutant dispersion with the observed levels based on self-reported emissions for fourteen facilities in Texas, United States. Results of numerical simulations demonstrate that neglecting the obstacle effect can underpredict (overpredict) the near-(far-)field concentrations for a low source. For a source located above obstacles, underprediction occurs at all distances. The diagnostic framework is applied to 107 self-reported upset emission events for fourteen petroleum refineries in Texas from year 2019–2022. Considering different metrics across all events, it can be concluded that the modeled concentrations based on self-reported emissions likely underpredict the observed concentration increments. Depending on the possible source height, the median factor of underprediction ranges from 3 to 95 based on the average-plume metric. The agreement between model and observation is better for events characterized by high emission amounts and rates, which also correspond to high observed concentration increments. Overall, the research highlights the importance of considering obstacles and demonstrates the potential application of the current approach as an efficient diagnostic method for self-reported upset emissions using fenceline observations of HAPs.

In order to evaluate the impact of traffic emissions on urban air quality, an increasing number of cities have established near-road air quality monitoring stations (hereafter referred to as roadside stations). This study reviews the system of roadside stations, the data application, and the evolution of air pollutant concentrations in the traffic environment in typical cities, and proposes optimization suggestions roadside stations in the future. The results show a steady increase in publications on roadside stations over the years, with the annual average number of publications after 2020 being approximately 10 times the annual mean during 1994–2001. The literature mainly focused on ‘air pollution’, ‘particulate matter’, ‘emission’, etc., highlighting the impact of traffic emissions on urban air quality and human health. The purpose and principles of setting up roadside stations vary from country to country, but they are mainly used to assess the impact of vehicle emissions on air quality and to protect human health in the vicinity of roads. Over the past decade, near-road NO₂ concentrations in typical cities have decreased by 30%–50%, although they remain higher than those observed in the urban atmosphere. The comprehensive analysis based on long-term data from roadside stations can provide insight into the effectiveness of vehicle emission control measures, and serve as a scientific basis for the formulation of future public health protection policies.

Emissions from wood-burning stoves contribute to local air pollution. However, it is difficult to determine the real emissions from such stoves, especially due to unknown user behaviour, which can have a large impact on emissions. In this study, the low-cost emission reduction measure “user training” was evaluated to determine its emission reduction potential on firewood stoves. Two sets of tests were carried out. First, a field measurement campaign was conducted in Styria (Austria) with four wood stoves, where gaseous and particulate emissions were measured before and after a user training on optimised heating behaviour (e.g. ignition mode, fuel properties and placement in the combustion chamber, air supply). Gaseous emissions (carbon monoxide – CO, organic gaseous compounds – OGC) were measured continuously, while particulates were measured in batches, in undiluted and hot as well as in diluted and cooled flue gas in parallel with a specific field measurement setup. In addition, particle filters were analysed to quantify the concentration of the carcinogenic compound benzo(a)pyrene (BaP). Second, user training workshops were conducted. These tests had a simple measurement setup in order to increase the number of tests. Thus, only CO emissions were evaluated.

The results show that real life emissions in the field are high and have a high variability compared to laboratory tests and official type test results. However, user training showed a significant reduction of CO, OGC, TSP and BaP emissions of 42%, 57%, 45% and 76% (median), respectively. In addition, TSP_sum (sum of hot and cooled particle emission samples) emissions decreased by 39% (median) after user training. The relative reduction rates of all batches show that the highest emission reduction potential was identified for BaP, with a reduction rate of up to 97%. The results of the workshop tests confirmed the high variability in user behavior and the range for the emission reduction potentials, with a median CO reduction of 41%.

The emission reduction potential of the user training measure is comparable to state-of-the-art technological measures such as electrostatic precipitators and catalysts. However, these measures are costly and require a high level of technical sophistication. User training, on the other hand, is relatively cheap, easy to implement and suitable for all users. Of course, there is some risk that trained end-users will revert to their old habits, leading to higher emissions again. Therefore, regular training may be necessary to maintain the higher level of performance. As we did not assess this aspect in our work, further research would be needed to prove this theory.

The identification and reduction of methane sources is considered an important part of the fight to stem greenhouse gas (GHG) emissions to the atmosphere. One of the largest industrial contributors to national GHG emissions in Canada is the Alberta Oil Sands Region. To quantify and investigate the spatial distribution and temporal variability of methane emissions from this region, airborne measurements were conducted in 2017 and 2018 with three aircraft. 59 flights were conducted in total to assess emissions for both open-pit and in-situ facilities, in both cold and warm seasons. Derived emission rates were higher than those reported in national inventories by 30%–96% depending on the facility. In-situ facilities had emission rates an order of magnitude lower than surface mining operations and differed significantly from inventory estimates. No statistical differences in CH₄ emissions between cold and warm seasons were observed, substantiating the use of simple upscaling to annual emissions within inventories. Rather than confirming a reported decrease in emissions between 2013 and 2018, the measurements suggest essentially no change from the 18 t h⁻¹ for the region observed in 2013. Overall, the results suggest that current methods of CH₄ emission determination within the oil sands region, for use in reporting, require improvement.

Air quality in cities with large maritime ports is considerably impacted by emissions from shipping activity which is of a growing relevance due to an increasing relative contribution. To explore the extent of shipping emissions to ambient air quality, simulations with the chemical transport model LOTOS-EUROS (LOng Term Ozone Simulation – EURopean Operational Smog model) were performed for the year 2018 at an approximate 1 × 1 km resolution for six European cities with large ports, i.e., Rotterdam, Antwerp, Hamburg, Amsterdam, Le Havre, and London. It was found that depending on the investigated city, 6.5%–62% of the nitrogen dioxide (NO₂) concentration in the city centres is attributable to shipping activities. This corresponds to contributions of 1.8–11.5 μg/m³ to the ambient air NO₂ concentrations. The average NO₂ contribution of shipping in these six cities was 28% (7.1 μg/m³). The largest relative contribution was found for Le Havre where 62% (10.8 μg/m³) of the annual average NO₂ concentration was caused by shipping emissions. The largest absolute contribution is found for the city centre of Hamburg with 11.5 μg/m³ (41%). The lowest absolute and relative contribution (respectively 1.8 μg/m³ and 6.5%) are found for London, also having the smallest port in terms of tonnage throughput, which is one of the influential factors that determine emission totals, investigated in this study. For the other investigated pollutants, i.e., PM_2.5, PM₁₀ and SO₂, contributions from shipping were less pronounced with average contribution for all cities of 10% (1.2 μg/m³) 7% (1.5 μg/m³) and 4% (0.16 μg/m³) respectively. To assess the effect of model choices on these results, this study also looked into the choice of simulation resolution and relations between meteorological parameters and NO₂ concentrations. Following simulations with varying chemical transport model resolutions (1 × 1 km to 24 × 24 km), it is found that a decrease in ambient air pollutant concentrations away from localized emission sources is more pronounced at higher (1 × 1 km) model resolutions and source contributions are influenced more significantly than total concentrations. Considering meteorology, generally low wind speeds (1–2 m/s) lead to high NO₂ concentration in city centres. For the cities where the port is much closer to the city centre (e.g., London, Le Havre, Hamburg and Antwerp) the absolute NO₂ concentrations as well as the contributions from shipping emissions become highest for windless conditions. The high concentrations (>60 μg/m³ NO₂) only occur when wind speeds fall below 6 m/s.

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.

Thailand experiences severe air quality issues, predominantly due to PM_2.5 pollution that surpasses WHO guidelines. The main sources are attributed to energy production, industrial activities, vehicular emissions, agricultural burning, and transboundary transport of pollutants. Understanding the transport and transformation of these pollutants is necessary for addressing air quality issues. The Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) provides information about meteorology, chemical reactions, and transport of trace gases and aerosols. The accuracy of WRF-Chem simulations greatly depends on the choice of anthropogenic and biomass burning emissions inventories. This study provides a detailed evaluation of these inventories to model PM_2.5 concentrations in Thailand during both haze and off-haze seasons in 2019. We evaluated WRF-Chem using four anthropogenic emission inventories—CAMS-GLOB-ANT, ECLIPSE, HTAP, and REAS—and four biomass burning emissions inventories—FINN1.5, FINN2.5 MOD, FINN2.5 MODVAR, and QFED—using data from ground-based air quality stations, MODIS AOD, and MOPITT CO satellite data. Our findings suggest CAMS-GLOB-ANT performs optimally for North Thailand, while HTAP and REAS are more effective in Eastern Thailand. For biomass burning, FINN1.5 shows superior performance. The study also highlights the challenge in capturing PM_2.5 diurnal variability, particularly due to inaccuracies in simulating the planetary boundary layer height during nighttime in complex terrains. Moreover, our analysis exhibits moderate model performances during the off-haze season while using global and regional anthropogenic emissions in Thailand, emphasizing the need for improving anthropogenic inventories for reliable air quality prediction. For biomass burning emissions, updating emission factors to reflect Thailand's specific vegetation types is recommended to improve WRF-Chem's representation of PM_2.5 levels.

Liquefied natural gas (LNG) use as shipping fuel has increased in recent years. While LNG results in lower carbon dioxide (CO₂) emissions as well as benefits in terms of air pollutants, the slip of unburned methane, the main component of LNG, has remained a concern. In this study, methane together with other climate warming agents, CO₂ and black carbon (BC), as well as other emission compounds were characterized from 4-stroke low-pressure dual fuel engine on-board a newly build cruise ship utilizing LNG as well as marine gas oil (MGO). The brake specific methane slip was found to vary according to engine load, being 2.3–3.0 g/kWh at 54–80% loads, but increasing to 10 g/kWh at 25% load and 21 g/kWh at 12% load. The LNG combustion also resulted in higher formaldehyde emissions compared to MGO, but reduction in formaldehyde levels was observed over the SCR catalyst present in the exhaust line of the dual-fuel engine, without urea injection, suggesting it may provide a pathway for formaldehyde mitigation. In terms of particle emissions, LNG use reduced particle mass (PM) by 87–93% and BC by 94–99% compared to MGO combustion. Non-volatile particle number above 23 nm (PN_nv,>23nm) and 10 nm (PN_nv,>10nm) were reduced by 88–97% and 97–99%, except at lowest engine load where PN_nv,>10nm increased by 26% compared to MGO utilization. When total greenhouse gas (GHG) emissions including CO₂ and BC were considered, LNG use resulted in 13–15% lower GHG at high loads, but the benefit was undermined by the escaping methane at low load conditions. Following the engine activity profile during 8-months of vessel operation on the Mediterranean suggested, however, that in a diesel-electric cruise ship, low load conditions are used mainly during arrivals and departures from harbors, as the engine was operated at loads above 40% for 90% of the operation time. Weighted emission factor, representing the actual engine operation, resulted in methane slip of 2.8 g/kWh or 1.7% of the fuel use, which is below the value considered in the FuelEU Maritime. The results suggest that load specific methane slip, together with engine load profile should be considered when evaluating methane slip on vessel or fleet level.

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.