A comprehensive assessment of energy use, environmental degradation, and economic progress can play a significant role in transition towards low-carbon economy, and it can serve as a reference for the green economic development for the rest of the developing world. The objective of this paper is to empirically investigate the current status of conventional and renewable energy use and environmental degradation. Following this, we have analyzed the decoupling relation among environmental degradation, energy use, and economic progress in Pakistan. The study adopted the comprehensive data from year 1972-2017 and applied Tapio decoupling method to explore the decoupling status of environmental degradation, energy use, and economic progress in Pakistan. The key finding from the study shows that the overall value of carbon emissions in Pakistan is relatively increasing with the passage of time and shows about 5.26% average growth rate which is creating severe environmental degradation. There were observed several fluctuations in the trend of carbon emissions which is basically due to the policy changes in the country. From the decoupling point of view, we found the decoupling linkage between energy use and carbon emissions that is growth negative decoupling, whereas a weak decoupling relation has been observed among carbon emissions and economic progress which means that in most of the year's county has achieved more economic growth compared with the carbon emissions. In addition, the similar weak decoupling relationship was found among energy use and economic progress. In the light of these findings, it is suggested to policymakers to promote technological advancement and alternate energy that will not only improve environmental quality, but it will also promote a low-carbon economy.The aim of the present study was to compare the prevalence of dental caries between crack cocaine users and a control group. The study included 106 participants in each group matched for age, sex, and exposure to tobacco. Crack cocaine users were selected from institutions for the treatment of chemical dependency, and the control group was recruited from a public school and among patients who sought dental care. A calibrated examiner determined dental caries experience [Decayed, Missing and Filled Teeth (DMFT) index]. The severity of tooth decay was determined using the Significant Caries Index (SiC). The prevalence of dental caries (DMFT ≥ 1) was 96.2 and 81.1% among the crack cocaine users and nonusers, respectively. Crack users had higher mean DMFT values (7.16 versus 4.92) for the decayed and missing components as well as a higher percentage of individuals with highly severe caries compared to nonusers. After the adjustments in the multivariate model, the prevalence of caries was 18% higher among the crack users (prevalence ratio 1.18; 95% confidence interval 1.08-1.30). Age, family income, crack cocaine use, and dental calculus were associated with the occurrence of dental caries. In conclusion, the prevalence of caries was higher among the crack users compared with the control group and remained associated with dental caries in the multivariate analysis.PM2.5 concentrations are commonly estimated using geographically weighted regression (GWR) models, but these models may suffer from multi-collinearity and over-focus on local feature problems. To overcome these shortcomings, a self-adaptive bandwidth eigenvector spatial filtering (SA-ESF) model utilizing the golden section search (GO-ESF) and genetic algorithm (GA-ESF) was proposed. The SA-ESF model was applied to estimate ground PM2.5 concentrations in the Yangtze River Delta (YRD) region of China both seasonally and annually from December 2015 to November 2016 using remotely sensing data, factory locations, and road networks. The results of the original eigenvector spatial filtering (ESF), GO-ESF, GA-ESF, and GWR models show that the GA-ESF model offers better performance and exhibits a better average adjusted R2 which is 26.6%, 15.3%, and 10.8% higher than for the ESF, GO-ESF, and GWR models, respectively. We next calculated stochastic site indicators that can describe characteristics of regional concentration from interpolated concentration maps derived from the GA-ESF and GWR models. The concentration maps and stochastic site indicators point to major differences in the PM2.5 concentrations in mountainous areas. There are notably high concentrations in those areas using the GWR model, in contrast with the GA-ESF results, indicating that there may be overfitting problems using the GWR model. Overall, the proposed SA-ESF model with the genetic algorithm technique can capture both global and local features and achieve promising results.Hexachlorocyclohexane (HCH), a typical organochloride pesticide, is one of the persistent organic pollutants. Despite the ban on technical grade HCH, it has been continuously observed at a steady level in the environment. The photochemical degradation of β-HCH in snow and ice under ultraviolet (UV) irradiation was investigated in this study. The effects of pH as well as common chemical components in snow on the degradation kinetics were investigated. https://www.selleckchem.com/products/lenumlostat.html In addition, the photodegradation products were determined and the reaction mechanism was hypothesized. The results showed that under UV irradiation, β-HCH can be photolyzed in snow and ice, with the photochemical degradation process conforming to the first-order kinetic equation. Changing the pH and adding Fe2+ had minimal effect on the photochemical degradation kinetics, while the presence of acetone, NO2-, NO3- and Fe3+ significantly inhibited the process. The addition of hydrogen peroxide slightly inhibited the photochemical degradation of β-HCH. Finally, the reaction rate, products and degradation mechanism of β-HCH in snow were compared with those in the ice phase. The photochemical degradation rate of β-HCH in snow was approximately 24 times faster than that in the ice phase. The photolysis product of β-HCH in snow was α-HCH, produced by the isomerization of β-HCH. However, in ice, in addition to α-HCH, pentachlorocyclohexene was produced by dechlorination. The results of this study are helpful in understanding the transformation of organochlorine pesticides in snow and ice, as well as in providing a theoretical basis for snow and ice pollution prevention and control.