Atmospheric Environment: X最新文献

Mountain observatories at high altitudes, far from local anthropogenic emission sources, are considered ideal for monitoring temporal variations of tropospheric air pollutants. During August 17–21 in 2013, we measured sulfur dioxide and nitric acid concentrations in the free troposphere at the top (3776 m a.s.l.) of Mt. Fuji, Japan. A parallel plate wet denuder-ion chromatographic system was used for the online measurement with 15 min temporal resolution. The continuous observations were successfully achieved without any problems. Of the samples collected, 97.8% of sulfur dioxide and 75.7% of nitric acid were above the limits of quantification. The average gas concentrations ± standard deviation (n = 408) were 0.106 ± 0.377 ppbv for sulfur dioxide and 0.015 ± 0.014 ppbv for nitric acid, respectively. Episodic elevations of sulfur dioxide concentration were recorded from August 20 to 21. Backward trajectory analyses indicated that the high-temporal resolution monitor detected the volcanic sulfur dioxide from Mt. Sakurajima, located 857 km from the sampling point. High-time-resolved observations in the free troposphere proved useful for source identification of air pollutants.

Ethylene oxide (EtO) is a hazardous air pollutant that can be emitted from a variety of difficult to measure industrial sources, such as fugitive leaks, wastewater handling, and episodic releases. Emerging next generation emission measurement (NGEM) approaches capable of time-resolved, low parts per billion by volume (ppbv) method detection limits (MDLs) can help facilities understand and reduce EtO and other air pollutant emissions from these sources yielding a range of environmental and public health benefits. In October 2021, a first of its kind 4-day observational study was conducted at an EtO chemical facility in the midwestern United States. The study had dual objectives to both improve understanding of EtO emission sources within the facility and advance NGEM methods. Using cavity ring-down spectroscopy (CRDS) instruments, a combination of mobile surveys and stationary multipoint process unit monitoring assessed EtO concentrations in and near facility operations, while testing and comparing measurement methods. The study concluded that four main areas of EtO source emissions existed within the facility, each possessing unique emission characteristics. Episodic EtO emissions from supply railcar switchovers and batch reactor washouts, lasting seconds to minutes in duration, produced EtO concentrations exceeding 500 ppbv inside the process unit in some cases. In one instance, EtO at ∼30 ppbv was briefly observed hundreds of meters from the process unit. Lower level but more sustained EtO concentrations were observed near an EtO transfer pump and wastewater tank outfall and drain system. Overall, 4.6% of mobile survey data were above the 1.2 ppbv mobile test MDL while the nine stationary sampling locations ranged from 17.7% to 82.8% of data above the 1.0 ppbv multipoint test MDL. This paper describes the EtO emissions observed in and near the four defined source areas within the facility and provides details of the NGEM method development advances accomplished as part of the study.

This study coupled a meteorological model with explicit bulk lightning and chemical transport models to investigate the impacts of lightning-induced nitrogen oxides (LNO_x) on nitrogen monoxide (NO), nitrogen dioxide (NO₂), and total reactive nitrogen oxide (NO_y) measured on August 22, 2017, at the top of Mt. Fuji, Japan. Our simulation results indicated that the LNO_x emitted around Wakasa Bay in the windward area of Mt. Fuji largely contributed to the NO_y content measured at the top of Mt. Fuji. Furthermore, sensitivity experiments regarding the height of LNO_x emissions indicated that the NO_y content measured atop Mt. Fuji originated from LNO_x emitted below 6 km. Our simulation assumed that a two-mode vertical distribution of LNO_x emissions was more consistent with measured NO_y at Mt. Fuji than a single-mode structure assumption in this case. A comparison of simulated NO_x (= NO + NO₂) and measured NO_x at Mt. Fuji indicated that the reaction rates of the NO and NO₂ cycles were well reproduced in our model; however, the ratio of NO_z (NO_y species other than NO_x) to NO_y estimated by the model were lower than the observed value, implying that the model either underestimated the reaction rate of LNO_x or overestimated the wet removal of lightning-induced NO_z. Finally, our results also suggest that the simultaneous observation of NO_y and NO_x is important for understanding LNO_x emissions, subsequent atmospheric chemical reactions, and removal processes, as well as validating chemical transport models.

We use the WRF-Chem atmospheric chemical transport model, driven by local emission inventories, to quantify the contribution of on-road transport emissions to surface PM_2.5 over Delhi during the post-monsoon season. We compare this contribution to other local (within Delhi) and regional (within the broader National Capital Region, NCR) anthropogenic sectors during the post-monsoon period when seasonal burning and stagnating meteorological conditions exacerbate baseline pollution levels. We find that local on-road transport contributes approximately 10% to daily mean PM_2.5 over Delhi, rising to 17% if regional on-road transport sources in the NCR are included. The largest individual contributions to Delhi daily mean PM_2.5 are from regional power and industry (14%) and domestic (11%) sectors, dominating nighttime and almost all daytime concentrations. Long range transport contribution from sources beyond the NCR is found to account for approximately 40%. The contribution from the local on-road transport sector to diurnal mean PM_2.5 is largest (18%) during the evening traffic peak. It is dominated by contributions from two- and three-wheelers (50%) followed by heavy-duty vehicles (30%), which also collectively represent 60–70% of the total on-road transport sector at any hour of the day. The combined contribution from passenger cars and light duty vehicles and from resuspended road dust to daily mean PM_2.5 is small (20%). Our work highlights two important factors which need to be considered in developing effective policies to meet PM_2.5 air quality standards in Delhi during post-monsoon. First, a multi-sector and multi-scale approach is needed, which prioritise the reduction in local transport emissions within Delhi, and, in the order, regional industries, domestic and transport emissions from NCR. Second, two-and three-wheelers and heavy-duty vehicles dominate on-road transport impact to PM_2.5, thus reductions from these vehicles should be given priority, both within Delhi and in the NCR.

Diesel engines contribute significantly to deteriorating air quality. Tightening legislation has led to various technological advances, but developments differ between countries. In India, air quality has not improved and fine particle (PM_2.5) related premature deaths are predicted to increase. In this study, we characterized the particle emissions of an Indian-manufactured BS IV (Bharat Stage, comparable to Euro emission standards) heavy-duty diesel vehicle and studied the effects of different fuels, fuel blends and lubricating oils. The main aims of the study were to investigate the particle emission dependency on fuel types and fuel blends used in India and to produce useful data for further use (e.g. legislative parties and modeling): emission factors (PN, PM, BC, other chemical compounds), size distributions and volatility of particles. Additionally, the sensitivity of the emissions to the lubricating oil choice was studied. Two lubricating oils, two fossil fuels conforming to BS IV and BS VI emission standards and two biofuel – BS IV fossil fuel blends were tested, one containing Renewable Paraffinic Diesel (RPD) and the other renewable Fatty Acid Methyl Ester (r-FAME). The tests were conducted on a chassis dynamometer (Delhi Bus Driving Cycle, DBDC). Our results show that the emitted particles were in ultrafine particle size range, and both the soot mode particles and smaller nanoparticles were affected by fuels and lubricating oils. The transition from BS IV grade diesel to BSVI was shown to have potential in reducing particle emissions (PN and eBC) of heavy-duty diesel vehicles in India. Blending fossil fuel with biofuel strongly affected particle number emissions, chemical composition, and eBC emissions and the emissions were highly sensitive to biofuel type. Changing the lubricating oil had a comparable magnitude of effect as changing the fuel and the results indicate that in order to reduce particle emissions, a combination of fuel and lubricating oil should be chosen, instead of choosing them separately.

Methane emissions from natural gas production are of increasing importance as they threaten efforts to mitigate climate change. Current inventory estimates carry high uncertainties due to difficulties in measuring emission sources across large regions. Satellite measurements of atmospheric methane could provide new understanding of emissions. This paper provides insight into the effectiveness of using satellite data to inform and improve methane inventories for natural gas activities. TROPOMI data are used to quantify methane emissions from natural gas within the Montney basin region of Canada and results are compared with existing inventories. Emissions estimated using TROPOMI data were 2.6 ± 2.2 kt/day which is 7.4 ± 6.4 times the inventory estimates. Pixels (7 by 7 km) that contained gas facilities had on average 11 ppb more methane than the background. 7.4% of pixels containing gas sites displayed consistently high methane levels that were not reflected in the inventory. The satellite data were not sufficiently granular to correlate with inventories on a facility scale. This illustrates the spatial limitations of using satellite data to corroborate bottom-up inventories.

The recent rise in the number of aircraft flights and the subsequent increase in emissions has raised concerns worldwide, and this increasing trend is expected to continue. This research provides an overall estimation of the landing and take-off cycle (LTO) emissions from Tribhuvan International Airport (TIA) as well as the associated contribution of these emissions to ambient air quality in Kathmandu valley. The aircraft emissions of nitrogen oxides (NO_x), carbon monoxide (CO), hydrocarbon (HC), sulfur dioxide (SO₂), volatile organic compounds (VOCs), particulate matter (PM₁₀, and PM_2.5), and black carbon (BC) during the LTO are estimated for recent 20 years by using the emission factor method. The corresponding contribution to ambient air quality was simulated using AERMOD and Weather Research and Forecasting (WRF) models. The research reveals that total LTO emissions by aircraft at TIA range from 898 to 2123 tonnes per year (2000–2019). The average LTO emissions of NO_x, CO, HC, VOC, SO₂, PM₁₀, PM_2.5, and BC were around 14512, 8142, 2387, 1737, 1247, 481, 472, and 231 tonnes respectively during the period of 20 years. The highest aircraft emission was shown in taxi/idle mode for the LTO cycle, with major constituents being HC and CO. The LTO emissions and their effect on air quality have continually increased. The highest contribution of the LTO emissions on air quality was found in the pre-monsoon season. The dominant pollutants in TIA were nitrogen oxides and its average 24-h concentration was 158.1 μg/m³, which exceeded the National Ambient Air Quality Standard (NAAQS) limit value. Hence, LTO emission significantly contributed to ambient air quality in Kathmandu city.

Ammonia emissions to the atmosphere have a range of negative impacts on environmental quality, human health, and biodiversity. Despite the considerable efforts in quantifying spatially explicit ammonia emissions, there are significant uncertainties in ammonia emission estimates at regional scales. We aimed to improve the modeling of atmospheric ammonia emission variability in space and time across the Netherlands by updating an agricultural ammonia emission model with a newly derived high-resolution crop map and a livestock housing location database of the Netherlands. To generate a crop map of 12 agricultural land cover classes, we applied random forest classification to the multi-temporal multispectral observations of surface reflectance and vegetation indices derived from Sentinel-2. The crop statistics were used to calculate ammonia emission distribution based on nitrogen demand (manure and mineral fertilizer needed) of different crop types using the INTEGRATOR model. Next, the crop map was utilized to spatially allocate the ammonia emissions to a high-resolution grid across the Netherlands. In addition, ammonia emissions from livestock housing systems were introduced as point sources using location data from the Geographic Information Agricultural Business system. The temporal emission variability was updated using a recently developed TIMELINES module. After the spatial and temporal distribution of ammonia emission was obtained with the crop map and housing information, it was imported into the chemistry transport model LOTOS-EUROS to model ammonia surface concentration for validation with in situ measurements.

The performed crop classification has an average accuracy score of 0.73. The derived crop map was compared with Dutch national statistics, and the results showed that the absolute median of the relative difference between Sentinel-2 derived crop areas and national statistical information is around 5%. The newly modeled ammonia monthly surface concentrations compared better with in situ measurements in terms of the magnitude and temporal variability than those derived from the original emission distribution, indicating that the temporal distribution of ammonia emissions was improved. The comparison of modeled and measured annual averaged surface concentrations illustrated that the spatial distribution of ammonia emission was also improved. All model performance indicators significantly improved, and the performance of the updated model was more stable and robust. The improvement was more evident at the stations where livestock housing is the main emission source. This study illustrates that apart from a satellite-derived crop map, information on the locations of animal housing systems also plays an essential role in better estimates of the spatial and temporal distribution of ammonia emissions. It can be worthwhile to extrapolate the method to other regions in Europe and elsewhere.