Sulphur study of the Palaeogene coals from Jaintia Hills, Meghalaya, NE India: Implications for palaeoenvironment, utilisation prospects, and environmental impacts

Coal samples of Eocene age (Shella Formation) from four different mines (Bapung, Jaintia, Sutunga, and Lakadong) of the Jaintia Hills of Meghalaya, Northeast India, were collected and investigated to observe the sulphur content and to understand the palaeoenvironment, utilisation prospects, and environmental impact. The study reveals that these coal samples contain sulphur in higher concentration (4.46% to 7.26%) both organic and inorganic forms. There are 3 coal seams exposed in the area. The organic sulphur is higher (2.53%-5.49%) than the inorganic forms (1.26%-1.77%). The upper seam is found to contain higher concentration of sulphur than the lower seam. Intra seam pyritic sulphur also shows an upward increasing trend. The high sulphur content in the coal seams suggests the marine influence in the peat-forming swamps. These coals are classified as High Sulphur coal (>1%) which is the main obstacle in the utilization although high volatile matter and hydrogen content strongly suggest that these coals are good for liquefaction. Moreover, during coal combustion emissions of sulphur dioxide produce acid rain, affecting the environment of the mine areas.

Bottom–Up Inventory of Residential Combustion Emissions in Poland for National Air Quality Modelling: Current Status and Perspectives

For many years, the Polish air quality modelling system was decentralized, which significantly hampered the appropriate development of methodologies, evaluations, and comparisons of modelling results. The major contributor to air pollution in Poland is the residential combustion sector. This paper demonstrates a novel methodology for residential emission estimation utilized for national air quality modelling and assessment. Our data were compared with EMEP and CAMS inventories, and despite some inequalities in country totals, spatial patterns were similar. We discuss the shortcomings of the presented method and draw conclusions for future improvements.

Contribution of on-road transportation to PM2.5

Fine particulate matter (PM2.5) mainly originates from combustion emissions. On-road transportation is considered one of the primary sources of PM2.5 emission. The relationship between on-road transportation and PM2.5 concentration varies temporally and spatially, and the estimation for this variation is important for policymaking. Here, we reveal the quantitative association of PM2.5 concentration with on-road transportation by the spatial panel Durbin model and the geographical and temporal weighted regression. We find that 6.17 billion kilometres (km) per km2 on-road transportation increase is associated with a 1-μg/m3 county-level PM2.5 concentration increase in the contiguous United States. On-road transportation marginally contributes to PM2.5, only 1.09% on average. Approximately 3605 premature deaths are attributed to PM2.5 from on-road transportation in 2010, and about a total of 50,223 premature deaths ascribe to PM2.5 taking 6.49% from 2003 to 2016. Our findings shed light on the necessity of the county-level policies considering the temporal and spatial variability of the relationship to further mitigate PM2.5 from on-road transportation.

Understanding the influence of combustion on atmospheric CO₂ over Europe by using satellite observations of CO₂ and reactive trace gases

We assess how nitrogen oxides (NOx = NO + NO2), carbon monoxide (CO) and formaldehyde (HCHO) can be used as proxies to determine the combustion contribution to atmospheric carbon dioxide (CO2) using satellite observations. We focus our analysis on 2018 when there is a full complement of column data from the TROPOspheric Monitoring Instrument (NO2, CO, and HCHO) and the Orbiting Carbon Observatory-2 (CO2). We use the nested GEOS-Chem atmospheric chemistry model to relate high-resolution emission inventories over Europe to these atmospheric data, taking into account scene-dependent averaging kernels. We find that that NO2 and CO are the better candidates to identify incomplete combustion and fingerprints of different combustion sectors, but both have their own challenges associated with properly describing their atmospheric chemistry. The secondary source of HCHO from oxidation of biogenic volatile organic compounds, particularly over southern European countries, compromises its use as a proxy for combustion emissions. We find a weak positive correlation between the CO : CO2 inventory ratio and observed column enhancements of ΔCO : ΔCO2 (R < 0.2), suggesting some consistency and linearity in CO chemistry and transport. However, we find a stronger negative correlation between the NOx : CO2 inventory ratio and observed column enhancements of ΔNO2 :ΔCO2 (R < 0.50), driven by non-linear photochemistry. Both of these observed ratios are described well by the GEOS-Chem atmospheric chemistry transport model, providing confidence of the quality of the emission inventory and that the model is a useful tool for interpreting these tracer-tracer ratios. Our results also provide some confidence in our ability to develop a robust method to infer combustion CO2 emission estimates using satellite observations of reactive trace gases that have up until now mostly been used to study surface air quality.

Feature Selection and Uncertainty Analysis for Bubbling Fluidized Bed Oxy-Fuel Combustion Data

This paper presents a novel feature extraction and validation technique for data-driven prediction of oxy-fuel combustion emissions in a bubbling fluidized bed experimental facility. The experimental data were analyzed and preprocessed to minimize the size of the data set while preserving patterns and variance and to find an optimal configuration of the feature vector. The Boruta Feature Selection Algorithm (BFSA) finds feature vector’s configuration and the Multiscale False Neighbours Analysis (MSFNA) is newly extended and proposed to validate the BFSA’s design for emission prediction to assure minimal uncertainty in mapping between feature vectors and corresponding outputs. The finding is that the standalone BFSA does not reflect various sampling period setups that appeared significantly influencing the false neighborhood in the design of feature vectors for possible emission prediction, and MSFNA resolves that.