scholarly journals Oil and gas impacts on air quality in federal lands in the Bakken region: an overview of the Bakken Air Quality Study and first results

2015 ◽  
Vol 15 (20) ◽  
pp. 28749-28792 ◽  
Author(s):  
A. J. Prenni ◽  
D. E. Day ◽  
A. R. Evanoski-Cole ◽  
B. C. Sive ◽  
A. Hecobian ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Oil And Gas ◽  
Quality Data ◽  
Gas Emissions ◽  
Horizontal Drilling ◽  
Federal Lands ◽  
Pollutant Concentrations ◽  
Quality Study ◽  
The Impact

Abstract. The Bakken formation contains billions of barrels of oil and gas trapped in rock and shale. Horizontal drilling and hydraulic fracturing methods have allowed for extraction of these resources, leading to exponential growth of oil production in the region over the past decade. Along with this development has come an increase in associated emissions to the atmosphere. Concern about potential impacts of these emissions on federal lands in the region prompted the National Park Service to sponsor the Bakken Air Quality Study over two winters in 2013–2014. Here we provide an overview of the study and present some initial results aimed at better understanding the impact of local oil and gas emissions on regional air quality. Data from the study, along with long term monitoring data, suggest that while power plants are still an important emissions source in the region, emissions from oil and gas activities are impacting ambient concentrations of nitrogen oxides and black carbon and may dominate recent observed trends in pollutant concentrations at some of the study sites. Measurements of volatile organic compounds also definitively show that oil and gas emissions were present in almost every air mass sampled over a period of more than four months.

scholarly journals Oil and gas impacts on air quality in federal lands in the Bakken region: an overview of the Bakken Air Quality Study and first results

2016 ◽  
Vol 16 (3) ◽  
pp. 1401-1416 ◽  
Author(s):  
A. J. Prenni ◽  
D. E. Day ◽  
A. R. Evanoski-Cole ◽  
B. C. Sive ◽  
A. Hecobian ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Oil And Gas ◽  
Quality Data ◽  
Gas Emissions ◽  
Horizontal Drilling ◽  
Federal Lands ◽  
Pollutant Concentrations ◽  
Quality Study ◽  
The Impact

Abstract. The Bakken formation contains billions of barrels of oil and gas trapped in rock and shale. Horizontal drilling and hydraulic fracturing methods have allowed for extraction of these resources, leading to exponential growth of oil production in the region over the past decade. Along with this development has come an increase in associated emissions to the atmosphere. Concern about potential impacts of these emissions on federal lands in the region prompted the National Park Service to sponsor the Bakken Air Quality Study over two winters in 2013–2014. Here we provide an overview of the study and present some initial results aimed at better understanding the impact of local oil and gas emissions on regional air quality. Data from the study, along with long-term monitoring data, suggest that while power plants are still an important emissions source in the region, emissions from oil and gas activities are impacting ambient concentrations of nitrogen oxides and black carbon and may dominate recent observed trends in pollutant concentrations at some of the study sites. Measurements of volatile organic compounds also definitively show that oil and gas emissions were present in almost every air mass sampled over a period of more than 4 months.

scholarly journals Positive effects of COVID-19 lockdown on air quality of industrial cities (Ankleshwar and Vapi) of Western India

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ritwik Nigam ◽  
Kanvi Pandya ◽  
Alvarinho J. Luis ◽  
Raja Sengupta ◽  
Mahender Kotha
Keyword(s):  
Air Quality ◽  
Meta Analysis ◽  
Linear Regression Analysis ◽  
Quality Data ◽  
Western India ◽  
Rapid Reduction ◽  
Positive Effects ◽  
Pollutant Concentrations ◽  
The Impact

AbstractOn January 30, 2020, India recorded its first COVID-19 positive case in Kerala, which was followed by a nationwide lockdown extended in four different phases from 25th March to 31st May, 2020, and an unlock period thereafter. The lockdown has led to colossal economic loss to India; however, it has come as a respite to the environment. Utilizing the air quality index (AQI) data recorded during this adverse time, the present study is undertaken to assess the impact of lockdown on the air quality of Ankleshwar and Vapi, Gujarat, India. The AQI data obtained from the Central Pollution Control Board was assessed for four lockdown phases. We compared air quality data for the unlock phase with a coinciding period in 2019 to determine the changes in pollutant concentrations during the lockdown, analyzing daily AQI data for six pollutants (PM10, PM2.5, CO, NO2, O3, and SO2). A meta-analysis of continuous data was performed to determine the mean and standard deviation of each lockdown phase, and their differences were computed in percentage in comparison to 2019; along with the linear correlation analysis and linear regression analysis to determine the relationship among the air pollutants and their trend for the lockdown days. The results revealed different patterns of gradual to a rapid reduction in most of the pollutant concentrations (PM10, PM2.5, CO, SO2), and an increment in ozone concentration was observed due to a drastic reduction in NO2 by 80.18%. Later, increases in other pollutants were also observed as the restrictions were eased during phase-4 and unlock 1. The comparison between the two cities found that factors like distance from the Arabian coast and different industrial setups played a vital role in different emission trends.

scholarly journals Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 76
Author(s):  
Estrella Lucena-Sánchez ◽  
Guido Sciavicco ◽  
Ionel Eduard Stan
Keyword(s):  
Air Quality ◽  
Fundamental Problem ◽  
Multivariate Time Series ◽  
Quality Data ◽  
Data Sets ◽  
Air Quality Modelling ◽  
Regression Problem ◽  
Car Traffic ◽  
Pollutant Concentrations ◽  
Language Selection

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.

scholarly journals Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

2021 ◽  
Author(s):  
Shwet Ketu ◽  
Pramod Kumar Mishra
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Classification Algorithm ◽  
Quality Data ◽  
Pollution Level ◽  
Classification Problems ◽  
Chi Square ◽  
The Impact

AbstractIn the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

scholarly journals Justification of integrated environmental and economic assessment of the impact actions in the field of oil and gas extraction

2017 ◽  
Vol 8 (4) ◽  
pp. 25-30
Author(s):  
Oleksandr Matsenko ◽  
Olga Gramma
Keyword(s):  
Energy Security ◽  
Power Plants ◽  
Oil And Gas ◽  
Economic Assessment ◽  
Well Being ◽  
Petroleum Products ◽  
Primary Energy ◽  
Infrastructure Development ◽  
Military Operations ◽  
The Impact

The aim of the welfare state, in accordance with the Constitution of Ukraine is to ensure conditions for the growth of welfare of citizens. One of the major components in the well-being of civilized societies is to ensure that citizens and businesses the necessary energy. Energy development is the basis for enhancing the social and economic living standards of the population and competitiveness. The key to this goal should be a reliable, economically viable and environmentally sound needs of the population and the economy of energy products. It is important to identify the critical factors is a threat to the energy security of the national economy. The state of the energy sector of Ukraine is negatively affected by continued dependence on imports of Russian natural gas, petroleum products and fuel for power plants. Today such dependence on primary energy, including coal, has become a leverage to Ukraine on the part of the neighboring state. The loss of the fuel and energy complex, and areas for future development of hydrocarbon resources as a result of the annexation of the Crimea and the military operations in the east of the country, as well as the destruction of the oil and gas infrastructure in the Donetsk and Luhansk regions, yielded additional new factors which significantly weakened the energy security of the country. Given the instability of strategic task for Ukraine, it is vital to achieve the highest possible level to ensure the economy’s own oil and gas resources, which, to a certain extent, will contribute to energy independence and savings of foreign exchange reserves of the country, as well as infrastructure development in the industry, tax revenues, creation of additional jobs.

scholarly journals Research on Air Quality Analysis Based on Cluster Methods

2021 ◽  
Vol 898 (1) ◽  
pp. 012024
Author(s):  
Zhaoni Li ◽  
Jian Zheng
Keyword(s):  
Air Quality ◽  
Ambient Air ◽  
Quality Analysis ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Republic Of China ◽  
Pollution Levels ◽  
Analysis Process ◽  
Cluster Methods ◽  
The Impact

Abstract Research on air quality analysis is a hot field. Here we describe an analysis process based on cluster methods for the data of ambient air quality. In this paper, we use the process to cluster on the air quality data which from the National Urban Air Quality Report in December 2020 on the official website of the Ministry of Ecology and Environment of the People’s Republic of China. We find that cities in different clusters with different main pollutants and pollution levels. Ambient air quality analysis aims to provide guidance for reducing the impact of air pollution on health.

scholarly journals Prediction of Environmental Pollution Using Hybrid PSO-K-Means Approach

2021 ◽  
Vol 12 (2) ◽  
pp. 65-76
Author(s):  
Manish Mahajan ◽  
Santosh Kumar ◽  
Bhasker Pant
Keyword(s):  
Air Quality ◽  
Environmental Pollution ◽  
Productivity Loss ◽  
Hybrid Approach ◽  
Quality Data ◽  
Quality Prediction ◽  
Clustering Methods ◽  
Air Quality Prediction ◽  
The Impact

Air pollution is increasing day by day, decreasing the world economy, degrading the quality of life, and resulting in a major productivity loss. At present, this is one of the most critical problems. It has a significant impact on human health and ecosystem. Reliable air quality prediction can reduce the impact it has on the nearby population and ecosystem; hence, improving air quality prediction is the prime objective for the society. The air quality data collected from sensors usually contains deviant values called outliers which have a significant detrimental effect on the quality of prediction and need to be detected and eliminated prior to decision making. The effectiveness of the outlier detection method and the clustering methods in turn depends on the effective and efficient choice of parameters like initial centroids and number of clusters, etc. The authors have explored the hybrid approach combining k-means clustering optimized with particle swarm optimization (PSO) to optimize the cluster formation, thereby improving the efficiency of the prediction of the environmental pollution.

scholarly journals Ambient air quality data reported at Sasol Secunda monitoring stations during COVID-19 lockdown – Mpumalanga, South Africa

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Andrew Venter ◽  
Sandra De Vos
Keyword(s):  
South Africa ◽  
Air Quality ◽  
Ambient Air ◽  
Human Behaviour ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Industrial Facilities ◽  
Ambient Temperatures ◽  
Pollutant Concentrations ◽  
Monitoring Stations

Various local and international research has been published on the effects of COVID-19 lockdown on ambient air quality. In most cases, a reduction in ambient NOx and PM concentrations have been observed with varying changes in ambient SO2 levels. Secunda, located in the Highveld Priority Area in Mpumalanga, South Africa is known for its large industrial facilities utilising coal as primary feedstock. The towns of Secunda and eMbalenhle provide the majority of the workforce to Sasol and has therefore been the focus of this study. The ambient air quality in the Secunda region was assessed due to the changes in human behaviour during lockdown, familiarity with the Sasol facility and the strategic locations of ambient air quality stations.Results show a clear decrease in ambient CO, NO2 and PM concentrations, especially during the first two weeks of lockdown. Only subtle changes were observed for ambient H2S and SO2 pollutant concentrations at the ambient monitoring stations. An increasing trend in all ambient species was observed towards the end and post lockdown, in contrast to declining ambient temperatures with the onset of winter. This is also contrary to the reduction in emissions from the factory that conducted annual maintenance in the month following lockdown (phase shutdown). This article concludes that human behaviour has a material local ambient impact on CO, NO2 and PM pollutant species, while H2S concentration profiles are more directly related to the industrial complex’s levels of activity. Ambient SO2 trends did not show a similar correlation with the facility’s activities (as H2S), but a stronger correlation was observed with the diverse local and regional sources in close proximity to Secunda and eMbalenhle. The influence of better dispersion especially on a local scale, brought about by more effective emission heights, is considered material. Moreover, meteorological factors, on local air quality, has been shown to be a material contributor to observed ambient air quality levels in the study domain

scholarly journals Aura OMI observations of regional SO<sub>2</sub> and NO<sub>2</sub> pollution changes from 2005 to 2014

2015 ◽  
Vol 15 (19) ◽  
pp. 26555-26607 ◽  
Author(s):  
N. A. Krotkov ◽  
C. A. McLinden ◽  
C. Li ◽  
L. N. Lamsal ◽  
E. A. Celarier ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Oil And Gas ◽  
Atmospheric Composition ◽  
Ozone Monitoring Instrument ◽  
Atmospheric Pollutant ◽  
Regulatory Policies ◽  
Industrial Sectors ◽  
Coal Power Plants ◽  
Coal Power

Abstract. The Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite has been providing global observations of the ozone layer and key atmospheric pollutant gases, such as nitrogen dioxide (NO2) and sulfur dioxide (SO2), since October 2004. The data products from the same instrument provide consistent spatial and temporal coverage and permit the study of anthropogenic and natural emissions on local-to-global scales. In this paper we examine changes in SO2 and NO2 over some of the world's most polluted industrialized regions during the first decade of OMI observations. In terms of regional pollution changes, we see both upward and downward trends, sometimes in opposite directions for NO2 and SO2, for the different study areas. The trends are, for the most part, associated with economic and/or technological changes in energy use, as well as regional regulatory policies. Over the eastern US, both NO2 and SO2 levels decreased dramatically from 2005 to 2014, by more than 40 and 80 %, respectively, as a result of both technological improvements and stricter regulations of emissions. OMI confirmed large reductions in SO2 over eastern Europe's largest coal power plants after installation of flue gas desulfurization devices. The North China Plain has the world's most severe SO2 pollution, but a decreasing trend has been observed since 2011, with about a 50 % reduction in 2012–2014, due to an economic slowdown and government efforts to restrain emissions from the power and industrial sectors. In contrast, India's SO2 and NO2 levels from coal power plants and smelters are growing at a fast pace, increasing by more than 100 and 50 %, respectively, from 2005 to 2014. Several SO2 hot spots observed over the Persian Gulf are probably related to oil and gas operations and indicate a possible underestimation of emissions from these sources in bottom-up emission inventories. Overall, OMI observations have proved to be very valuable in documenting rapid changes in air quality over different parts of the world during the last decade. The baseline established during the first 10 years of OMI is indispensable for the interpretation of air quality measurements from current and future satellite atmospheric composition missions.

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