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.
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 PM2.5 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 PM2.5 over Delhi, rising to 17% if regional on-road transport sources in the NCR are included. The largest individual contributions to Delhi daily mean PM2.5 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 PM2.5 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 PM2.5 is small (20%). Our work highlights two important factors which need to be considered in developing effective policies to meet PM2.5 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 PM2.5, 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 (PM2.5) 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.
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 (NOx), carbon monoxide (CO), hydrocarbon (HC), sulfur dioxide (SO2), volatile organic compounds (VOCs), particulate matter (PM10, and PM2.5), 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 NOx, CO, HC, VOC, SO2, PM10, PM2.5, 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.
This study presents the first analysis of the variability of atmospheric CO2 in the area of the Marseille city (France). It addresses the role of anthropogenic emissions, natural fluxes and atmospheric boundary layer height (ABLH) dynamics on CO2 variability at the diurnal, synoptic, seasonal and multi-annual scales. A regional network based on 4 in-situ observation sites of CO2, CO and NOx was deployed between 2013 and 2018. One urban site (CAV) located in Marseille center was set up in collaboration with the regional air quality monitoring agency ATMOSUD. A second site (SME) was installed at the coastal edge of Marseille at the border of the Mediterranean Sea. The two other sites belonging to the ICOS (integrated Carbon Observing System) national atmospheric greenhouse observation network, are located in natural areas at the Observatoire de Haute Provence (OHP, 80 km north of Marseille) and at Cape Corsica (ERSA, 330 km east of Marseille) and are defined as regional background sites. The comparison between the sites was performed on the period common to all sites (1 July 2016–13 February 2018). The datasets are calibrated on the reference World Meteorological Organization scales for CO2 and CO with high precision and accuracy levels. At all sites, the mean annual CO2 growth rate is found to be quite similar to the Mauna Loa (Hawaii) reference site one, but mean annual CO2 concentrations are higher of several ppm at both urban sites than at both background sites. The diurnal cycle shows a higher amplitude at the urban sites (14.5 ppm at SME; 18.8 ppm at CAV) than at the background sites (5.3 ppm at OHP; 0.5 ppm at ERSA), as in other urban studies. While the urban stations are influenced by large urban anthropogenic emissions (mostly from traffic and heating, especially in winter), both background sites are mainly influenced by natural fluxes. At ERSA, the CO2 diurnal cycle is found to be primarily controlled by the small air-sea CO2 fluxes. At OHP, the diurnal variability of CO2 is mainly driven by the activity of vegetation (photosynthesis and respiration) and ABLH dynamics. For similar reasons, atmospheric CO2 concentrations are also characterized by larger seasonal variations in the city (29.2 ppm at CAV and 20.3 ppm at SME, respectively) than at OHP (13.1 ppm) and at SME (13.9 ppm). The influence of local, regional and remote anthropogenic emissions is assessed through a classification of the datasets by wind conditions. Similarly to other urban studies, a dome of several tens of ppm of CO2 gets formed over the city at low wind speed (less than 4 m s−1). For higher wind speeds (4–10 m s−1), the influence of regional and remote emissions on atmospheric CO2 is function of wind direction, varying from a few ppm at the background sites to a plume of more than 10 ppm at the urban ones. F
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.
A common practice within the current regulatory framework for gas distribution uses an approach where operators report their network emissions by applying an average emission factor for all leaks, sometimes sorted by pipe material and type of assets. Such an approach has two drawbacks: first, it does not account for the specificities of the gas systems and the maintenance processes of the operators; and second, it does not enable the prioritization of large leaks that is key for an effective emissions abatement program. This article describes a method using a mobile leak detection and quantification system to assess methane emissions from a gas distribution network and to reduce them by accelerating the detection and repair of larger leaks. The approach allows for data-driven system-wide emissions quantification that is specific to the network and not subject to operator’s leak detection practices that may affect their traditional emission factor-based reporting. Furthermore, we show that for a sensor with a sufficiently low detection limit, the calculated emissions are independent of the precision of the measurement if the uncertainties are correctly addressed. Such a result is important because it assures that methane emissions estimates are not biased and can be used to assess the performance of abatement programs. Finally, we illustrate how the approach can be practically implemented through a program where the largest leaks are rapidly identified and repaired to abate methane emissions while minimizing costs.
Nanofibrous filter materials were prepared by electrospinning a solution of 28 wt% poly(vinylidene fluoride) in N,N-dimethylacetamide with and without the addition of 2 wt% AgNO3, Cu(NO3)2·2.5H2O or ZnCl2. X-ray diffraction, scanning electron microscopy with energy dispersive X-ray spectroscopy, inductively coupled plasma mass spectroscopy, thermogravimetric analysis, contact angle measurement, nitrogen sorption, and mercury intrusion porosimetry methods were used for the characterization of physical structure as well as the chemical composition of the electrospun materials. Particle filtration efficiency and antiviral activity against the SARS-CoV-2 alpha variant were tested in order to estimate the suitability of the prepared electrospun filter materials for application as indoor air filtration systems with virucidal properties. All filter materials prepared with salts demonstrated very high particle filtration efficiency (≥98.0%). The best antiviral activity was demonstrated by a material containing Cu(NO3)2·2.5H2O in the spinning solution, which displayed the decrease in the number of infectious virions by three orders of magnitude after a contact time of 12 h. Materials with the addition of AgNO3 and ZnCl2 decreased the number of infectious virions after the same contact time by only ∼8 and ∼11 times, respectively.
Field application of digestate from liquid animal manure (slurry) poses an environmental risk due to emission of ammonia (NH3). Various application techniques can reduce NH3 losses. Three experiments were conducted in Denmark where NH3 emission after field application of anaerobically digested separated cattle slurry was measured using dynamic flux chambers with online measurements. The results showed that acidifying the digestate at a low level equal to the crop sulphur requirement reduced the average NH3 emission by 13% compared to non-acidified trailing shoe application of digestate. Disc injection reduced average emissions even more by 38% compared to trailing shoe application. There was a large variation in mitigation effect between experiments.
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