How does economic complexity influence environmental degradation? New insights from African countries

This paper seeks to investigate the effect of economic complexity on environmental degradation in 20 selected African countries over the period 1991–2014. The Feasible Generalized Least Square (FGLS), and Driscoll-Kraay estimators (DK) are used to capture the objectives of the study. The results suggest that economic complexity and economic growth enhance environmental degradation. Also, natural resources rents and globalization improve environmental quality. Furthermore, the findings reject the U-shaped relationship between economic complexity and environmental degradation. In addition, the Dumitrescu-Hurlin panel causality test shows a bidirectional causal relation between economic complexity and environmental degradation. Taking into account the ecological deficit or ecological reserve status of country, it is shown that while the natural resource rents reduce environmental degradation in ecological reserve countries, they increase environmental degradation in ecological deficit countries. The results are robust when an alternative measure of economic complexity is used. Based on these findings, the paper suggests that the governments of African countries should take into account economic complexity when designing their environmental protection policies.     

Air Pollution Concentrations of PM2.5, PM10 and NO2 at Ambient and Kerbsite and Their Correlation in Metro City – Mumbai

Ambient concentrations of PM_2.5 and PM₁₀ are of concern with respect to effects on human health and environment. Increased levels of mortality and morbidity have been associated with respirable particulate air pollution. In India, it is not yet mandatory to monitor PM_2.5 levels therefore very limited information is available on PM_2.5 levels. To understand the fine particle pollution and also correlate with PM₁₀ which are monitored regularly in compliance with ambient air quality standards. This study was carried out to monitor PM_2.5, PM₁₀, and NO₂ for about one year in a residential cum commercial area of Mumbai city with a view to understand their correlation. The average PM_2.5 concentration at ambient and Kerbsite was 43 and 69 μg/m³. The correlation coefficients between PM_2.5 and PM₁₀ at ambient and Kerbsite were 0.83 and 0.85 respectively thus indicating that most of the PM_2.5 and PM₁₀ are from similar sources. TSP, PM₁₀ levels exceeded Central Pollution Control Board(CPCB) standard during winter season. PM_2.5 levels also exceeded 24 hourly average USEPA standard during winter season indicating unhealthy air quality.     

Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams,United States

Environmental Science and Pollution Research - Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important....     

Validating the effects of the environmental regulation compliance scale: evidence from the Nigerian oil and gas industry

Environmental Science and Pollution Research - Growing environmental deterioration has caused many countries to tighten their environmental regulations across the globe. Recent studies show that...     

Hydrochemical characteristics and identification of groundwater pollution sources in tropical savanna

Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of natural and anthropogenic processes. In this study, classic chemical and multivariate statistical methods were utilized to assess the groundwater quality and ascertain the potential contamination sources affecting the groundwater quality of Galma sub-watershed in a tropical savanna. For this purpose, the data set of 18 groundwater quality variables covering 57 different sampling boreholes (BH) was used. The groundwater samples essentially contained the cations in the following order of dominance: Ca²⁺ ?>?Na⁺ ?>?Mg²⁺ ?>?K⁺. However, the anions had HCO₃^–?>?Cl^–?>?SO₄^–2?>?NO₃^– respectively. The hydrochemical facies classified the groundwater types of the sub-watershed into mixed Ca–Mg–Cl type of water, which means no cations and anions exceeds 50%. The second dominant water type was Ca–Cl. The Mg–HCO₃ water type was found in BH 9, and Na–Cl water type in BH 29 of the studied area. The weathering of the basement rocks was responsible for the concentrations of these ions in the groundwater chemistry of the sub-watershed. Hierarchical cluster analysis (HCA) grouped the groundwater samples (boreholes) into five clusters that are statistically significant regarding the similarities of groundwater quality characteristics. The principal component analysis (PCA) extracted two major principal components explained around 65% of the variance and suggested the natural and anthropogenic processes especially the agricultural pollutants including synthetic fertilizers, and leaching of agricultural waste as the main factors affecting the groundwater quality. The integrated method proved to be efficient and robust for groundwater quality evaluation, as it guaranteed the precise assessment of groundwater chemistry in the sub-watershed of the tropical savanna. The findings of this investigation could be useful to the policy makers for developing effective groundwater management plans for the groundwater resources and protection of the sub-watershed.