Modeling Emissions Sources and Characterization in a Dense Low-Income Township Near Industrial Zones and Mine Dumps (Case of Tsakane, South Africa).

The sources of pollution in Tsakane township, which is situated within the City of Ekurhuleni in the province of Gauteng, South Africa, are investigated in this paper. The City of Ekurhuleni has the most industrial activities reported on South Africa's National Atmospheric Emission Inventory System (NAEIS), accounting for 40% of all listed activities in the country. The problem of suburban air pollution in South Africa is mainly associated with dense low-income areas like townships. The aim of this paper was to investigate atmospheric concentration correlation parameters, emissions roses, and probability modelling functions in order to analyse and classify significant emission sources affecting the township. Sulfur dioxide, nitrogen dioxide, ozone, and PM10 were the focus of the investigation. The probability functions for identifying and characterizing unknown or hidden sources of pollution were developed using hourly data. Furthermore, K-clustering algorithm analysis technique was used to provide graphical context for sources. PM10, ozone, sulfur dioxide, and nitrogen dioxide have all been identified as having directional pollution sources that are problematic and the results provide baseline data for a detailed understanding of current emission levels and possible sources.

Methylocystis silviterrae sp.nov., a high-affinity methanotrophic bacterium isolated from the boreal forest soil

A novel species is proposed for a high-affinity methanotrophic representative of the genus Methylocystis . Strain FST was isolated from a weakly acidic (pH 5.3) mixed forest soil of the southern Moscow area. Cells of FST are aerobic, Gram-negative, non-motile, curved coccoids or short rods that contain an intracytoplasmic membrane system typical of type-II methanotrophs. Only methane and methanol are used as carbon sources. FST grew at a temperature range of 4–37 °C (optimum 25–30 °C) and a pH range of 4.5 to 7.5 (optimum pH 6.0–6.5). The major fatty acids were C18 : 1ω8c, C18 : 1ω7c and C18 : 0; the major quinone as Q-8. FST displays 16S rRNA gene sequences similarity to other taxonomically recognized members of the genus Methylocystis, with Methylocystis hirsuta CSC1T (99.6 % similarity) and Methylocystis rosea SV97T (99.3 % similarity) as its closest relatives. The genome comprises 3.85 Mbp and has a DNA G+C content of 62.6 mol%. Genomic analyses and DNA–DNA relatedness with genome-sequenced members of the genus Methylocystis demonstrated that FST could be separated from its closest relatives. FST possesses two particulate methane monooxygenases (pMMO): low-affinity pMMO1 and high-affinity pMMO2. In laboratory experiments, it was demonstrated that FST might oxidize methane at atmospheric concentration. The genome contained various genes for nitrogen fixation, polyhydroxybutyrate synthesis, antibiotic resistance and detoxification of arsenic, cyanide and mercury. On the basis of genotypic, phenotypic and chemotaxonomic characteristics, it is proposed that the isolate represents a novel species, Methylocystis silviterrae sp. nov. The type strain is FST (=KCTC 82935T=VKM B-3535T).

Microalgae potential in the capture of CO2 emission

In a perspective projected to reduce the atmospheric concentration of greenhouse gases, in which carbon dioxide is the master, the use of microalgae is an effective and decisive response. The review describes the bio circularity of the process of abatement of carbon dioxide through biofixation in algal biomass, highlighting the potential of its reuse in the production of high value-added products.

Dominance of the residential sector in Chinese black carbon emissions as identified from downwind atmospheric observations during the COVID-19 pandemic

Emissions of black carbon (BC) particles from anthropogenic and natural sources contribute to climate change and human health impacts. Therefore, they need to be accurately quantified to develop an effective mitigation strategy. Although the spread of the emission flux estimates for China have recently narrowed under the constraints of atmospheric observations, consensus has not been reached regarding the dominant emission sector. Here, we quantified the contribution of the residential sector, as 64% (44–82%) in 2019, using the response of the observed atmospheric concentration in the outflowing air during Feb–Mar 2020, with the prevalence of the COVID-19 pandemic and restricted human activities over China. In detail, the BC emission fluxes, estimated after removing effects from meteorological variability, dropped only slightly (− 18%) during Feb–Mar 2020 from the levels in the previous year for selected air masses of Chinese origin, suggesting the contributions from the transport and industry sectors (36%) were smaller than the rest from the residential sector (64%). Carbon monoxide (CO) behaved differently, with larger emission reductions (− 35%) in the period Feb–Mar 2020, suggesting dominance of non-residential (i.e., transport and industry) sectors, which contributed 70% (48–100%) emission during 2019. The estimated BC/CO emission ratio for these sectors will help to further constrain bottom-up emission inventories. We comprehensively provide a clear scientific evidence supporting mitigation policies targeting reduction in residential BC emissions from China by demonstrating the economic feasibility using marginal abatement cost curves.

Quantification of CH₄ coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO₂ and Methane (CoMet) campaign

Methane (CH4) is the second most important anthropogenic greenhouse gas, whose atmospheric concentration is modulated by human-induced activities, and it has a larger global warming potential than carbon dioxide (CO2). Because of its short atmospheric lifetime relative to that of CO2, the reduction of the atmospheric abundance of CH4 is an attractive target for short-term climate mitigation strategies. However, reducing the atmospheric CH4 concentration requires a reduction of its emissions and, therefore, knowledge of its sources. For this reason, the CO2 and Methane (CoMet) campaign in May and June 2018 assessed emissions of one of the largest CH4 emission hot spots in Europe, the Upper Silesian Coal Basin (USCB) in southern Poland, using top-down approaches and inventory data. In this study, we will focus on CH4 column anomalies retrieved from spectral radiance observations, which were acquired by the 1D nadir-looking passive remote sensing Methane Airborne MAPper (MAMAP) instrument, using the weighting-function-modified differential optical absorption spectroscopy (WFM-DOAS) method. The column anomalies, combined with wind lidar measurements, are inverted to cross-sectional fluxes using a mass balance approach. With the help of these fluxes, reported emissions of small clusters of coal mine ventilation shafts are then assessed. The MAMAP CH4 column observations enable an accurate assignment of observed fluxes to small clusters of ventilation shafts. CH4 fluxes are estimated for four clusters with a total of 23 ventilation shafts, which are responsible for about 40 % of the total CH4 mining emissions in the target area. The observations were made during several overflights on different days. The final average CH4 fluxes for the single clusters (or sub-clusters) range from about 1 to 9 t CH4 h−1 at the time of the campaign. The fluxes observed at one cluster during different overflights vary by as much as 50 % of the average value. Associated errors (1σ) are usually between 15 % and 59 % of the average flux, depending mainly on the prevailing wind conditions, the number of flight tracks, and the magnitude of the flux itself. Comparison to known hourly emissions, where available, shows good agreement within the uncertainties. If only emissions reported annually are available for comparison with the observations, caution is advised due to possible fluctuations in emissions during a year or even within hours. To measure emissions even more precisely and to break them down further for allocation to individual shafts in a complex source region such as the USCB, imaging remote sensing instruments are recommended.

Methane in Zackenberg Valley, NE Greenland: multidecadal growing season fluxes of a high-Arctic tundra

The carbon balance of high-latitude terrestrial ecosystems plays an essential role in the atmospheric concentration of trace gases, including carbon dioxide (CO2) and methane (CH4). Increasing atmospheric methane levels have contributed to ∼ 20 % of the observed global warming since the pre-industrial era. Rising temperatures in the Arctic are expected to promote the release of methane from Arctic ecosystems. Still, existing methane flux measurement efforts are sparse and highly scattered, and further attempts to assess the landscape fluxes over multiple years are needed. Here we combine multi-year July–August methane flux monitoring (2006–2019) from automated flux chambers in the central fens of Zackenberg Valley, northeast Greenland, with several flux measurement campaigns on the most common vegetation types in the valley to estimate the landscape fluxes over 14 years. Methane fluxes based on manual chamber measurements are available from campaigns in 1997, 1999–2000, and in shorter periods from 2007–2013 and were summarized in several published studies. The landscape fluxes are calculated for the entire valley floor and a smaller subsection of the valley floor, containing the productive fen area, Rylekærene. When integrated for the valley floor, the estimated July–August landscape fluxes were low compared to the single previous estimate, while the landscape fluxes for Rylekærene were comparable to previous estimates. The valley floor was a net methane source during July–August, with estimated mean methane fluxes ranging from 0.18 to 0.67 mg m−2 h−1. The mean methane fluxes in the fen-rich Rylekærene were substantially higher, with fluxes ranging from 0.98 to 3.26 mg m−2 h−1. A 2017–2018 erosion event indicates that some fen and grassland areas in the center of the valley are becoming unstable following pronounced fluvial erosion and a prolonged period of permafrost warming. Although such physical disturbance in the landscape can disrupt the current ecosystem–atmosphere flux patterns, even pronounced future erosion of ice-rich areas is unlikely to impact methane fluxes on a landscape scale significantly. Instead, projected changes in future climate in the valley play a more critical role. The results show that multi-year landscape methane fluxes are highly variable on a landscape scale and stress the need for long-term spatially distributed measurements in the Arctic.

Investigating Environmental Kuznets Curve: A Panel Data Analysis for India

The main objective of this study is to examine the relevance of the environmental Kuznets curve (EKC) hypothesis in describing the relationship between air pollution and development of a panel of 21 Indian states, using data for the period 2001–2016. This article attempts to use panel unit root, the panel cointegration test and panel dynamic ordinary least square approach to examine the relationship among various variables, including the atmospheric concentration of sulphur dioxide (SO2)/nitrogen dioxide (NO2), net state domestic product, social sector expenditure and other variables used as a proxy for the composition effect and development effect. The empirical analysis indicates that there exists a long-term relationship between the concentration of SO2 and NO2 with per capita income and other variables. In terms of the EKC hypothesis, the findings recommend the existence of a cubic relationship in the long run and emphasise the need to bring environment-friendly structural changes in economic activity and to enhance sustainable development through technological innovation.

Prediction of Carbon Monoxide (CO) Atmospheric Pollution Concentrations with Machine Learning and Time Series Analysis in Langkawi, Malaysia

Carbon monoxide (CO) is a non-irritant toxic and odourless gas produced from the incomplete combustion of fossil fuels. Long-term exposures to lower levels of carbon monoxide have wide implications for human health. Thus, an early warning system for CO atmospheric concentration with an accurate and reliable forecasting method is crucial. Studies for predicting CO atmospheric concentration are still limited in Malaysia especially using data science approaches. This study aims to develop and predict future CO concentration for the next few hours by using the statistical time series approach and machine learning approach. The data used for the project is the air quality data of the monitoring station in Langkawi, Malaysia. The data mining tool used for this project is RapidMiner Studio. Based on the results, it showed that Time Series analysis with deep learning gave a reasonably good CO concentration prediction for the next 3 hours with a relative error of approximate 10%. The model developed in this project can be used by authorities as public health’s protection measure to provide an early alarm for alerting the Malaysian populations on the air pollution issue.