Forecasting the impact of meteorological parameters on air pollutants in Andhra Pradesh using machine learning techniques

In the 21st century, air pollution has emerged as a significant problem all over the globe due to a variety of activities carried out by humans, such as the acceleration of industrialization and urbanization. SO₂, NO₂, and NH₃ are the key components contributing to air pollution. Moreover, these air pollutants have a significant connection to several climatic characteristics, such as the speed of the wind, the relative humidity, the temperature, the amount of precipitation, and the surface pressure. As a result, machine learning (ML) is regarded as a more effective strategy for predicting air quality than more conventional approaches such as probability and statistics, among others. In the research, Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), and Multi-Linear Regression (MLR) algorithms are used to make predictions about air quality, and MSE (Mean Squared Error), RMSE (Root Mean Square Error), MAE (Mean Squared error), and R² are used to determine how accurate the predictions are.     

Association of benzene exposure with insulin resistance,SOD, and MDA as markers of oxidative stress in children and adolescents

Benzene is a ubiquitous environmental pollutant with various health effects. It is reported that benzene exposure might be associated with insulin resistance in elderly adults. The aim of this study is to investigate the association between urinary benzene metabolite, trans, trans-muconic acid (t,t-ma) and markers of oxidative stress and insulin resistance in children and adolescents. This cross-sectional study was conducted in 2017 among 86 children and adolescents, aged 6–18 years, living in Isfahan, Iran. t,t-ma was measured as urinary benzene metabolite and homeostasis model assessment (HOMA-IR) was determined as an index of insulin resistance. Moreover, malondialdehyde (MDA) and superoxide dismutase (SOD) were assessed as oxidative stress markers. We found significant association between insulin resistance, fasting blood glucose, and fasting blood insulin with t,t-ma (p values?=?0.002, 0.03, and 0.001, respectively). Results of this study indicate that benzene metabolite in higher concentrations in comparison with lower concentrations is associated with increased risk of insulin resistance. Moreover, after adjustment for age, sex, and household passive smoking, statistically significant increase were documented in SOD and MDA (4.49- and 3.54-fold, respectively) in intermediate levels of t,t-ma vs. low levels of t,t-ma (p values?=?0.01 and 0.034, respectively). To the best of our knowledge, this is the first study in its kind in the pediatric age group. It showed that benzene exposures, even in environmental levels, might be associated with insulin resistance and oxidative stress in children and adolescents. Further longitudinal studies are necessary to assess the clinical impacts of this finding.     

Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran)

Hyrcanian forests of North of Iran are of great importance in terms of various economic and environmental aspects. In this study, Spot-6 satellite images and regression models were applied to estimate above-ground biomass in these forests. This research was carried out in six compartments in three climatic (semi-arid to humid) types and two altitude classes. In the first step, ground sampling methods at the compartment level were used to estimate aboveground biomass (Mg/ha). Then, by reviewing the results of other studies, the most appropriate vegetation indices were selected. In this study, three indices of NDVI, RVI, and TVI were calculated. We investigated the relationship between the vegetation indices and aboveground biomass measured at sample-plot level. Based on the results, the relationship between aboveground biomass values and vegetation indices was a linear regression with the highest level of significance for NDVI in all compartments. Since at the compartment level the correlation coefficient between NDVI and aboveground biomass was the highest, NDVI was used for mapping aboveground biomass. According to the results of this study, biomass values were highly different in various climatic and altitudinal classes with the highest biomass value observed in humid climate and high-altitude class.     

Soil conservation practices and production efficiency of smallholder farms in Central China

The aim of this paper is to evaluate and analyze the impact of adoption of soil conservation practices (SCPs) on the technical efficiency of smallholder rice producers in Central China. We address self-selection bias and unobserved heterogeneity problems by estimating a switching regression model for the adoption decision function and separate stochastic production frontiers for SCP and Conventional farms while allowing for production inefficiency. SCP farms exhibit statistically higher average technical efficiency than Conventional farms. Education, extension services, membership in cooperatives, access to credit, and alternative income sources are positively and significantly associated with technical efficiency for both groups. Conventional farms display higher partial output elasticity for land, while only SCP farms show significant elasticity for capital.     

Groundwater depth and elevation interpolation by kriging methods in Mohr Basin of Fars province in Iran

Prediction of groundwater depth and elevation is important in quantitative water management especially in arid areas. There are several basins in southwest of Iran, in Zagross Mountain, in which the water wells are distributed along a narrow elliptic ring band around the region. To find the most applicable interpolation method, both of the groundwater depth and elevation are predicted by different kriging methods. It is found that the groundwater elevation and depth can be predicted by different methods. Furthermore, it is found that the methods in which the trend is eliminated predicted the groundwater elevation and depth in central part of the region is with less standard error. Furthermore, the methods with no trend elimination, predicted the groundwater depths with less error near the water wells. Dividing the area to hydro-geologically homogeneous sub-areas improved the interpolation precision.     

Mobility of selected trace elements in Mediterranean red soil amended with phosphogypsum: experimental study

Soil amendment by phosphogypsum (PG) application becomes of increasing importance in agriculture. This may lead, however, to soil, plant, and groundwater contamination with trace elements (TEs) inherently present in PG. Monitoring of selected TEs (Pb, Zn, Cu, and Cd) distribution and mobility in a Mediterranean red soil profile has been performed in soil parcels applied with PG over a 16-month period. Concentrations were measured in soil and plant samples collected from various depth intervals at different points in time. TEs sequential extraction was performed on soil and PG samples. Results showed soil profile enrichment peaked 5 months after PG application for Cd, and 12 months for Pb, Zn, and Cu. Rainwater, pH, total organic carbon, and cationic exchange capacity were the main controlling factors in TEs accumulation in soils. Cd was transferred to a soil depth of about 20 cm. Zn exhibited mobility towards deeper layers. Pb and Cu were accumulated in around 20-55-cm-deep layers. PG increased the solubility of the studied TEs; PG-applied soils contained TEs bound to exchangeable and acid-soluble fractions in higher percentages than reference soil. Pb, Zn, and Cu were sorbed into mineral soil phases, while Cd was mainly found in the exchangeable (bio-available) form. The order of TEs decreasing mobility was Zn > Cd > Pb > Cu. Roots and leaves of existed plants, Cichorium intybus L., accumulated high concentrations of Cd (1-2.4 mg/kg), exceeding recommended tolerable levels, and thus signifying potential health threats through contaminated crops. It was therefore recommended that PG should be applied in carefully established, monitored, and controlled quantities to agricultural soils.     

Water and waste load allocation in rivers with emphasis on agricultural return flows: application of fractional factorial analysis

In this paper, a new methodology is developed to handle parameter and input uncertainties in water and waste load allocation (WWLA) in rivers by using factorial interval optimization and the Soil, Water, Atmosphere, and Plant (SWAP) simulation model. A fractional factorial analysis is utilized to provide detailed effects of uncertain parameters and their interaction on the optimization model outputs. The number of required optimizations in a fractional factorial analysis can be much less than a complete sensitivity analysis. The most important uncertain inputs and parameters can be also selected using a fractional factorial analysis. The uncertainty of the selected inputs and parameters should be incorporated real time water and waste load allocation. The proposed methodology utilizes the SWAP simulation model to estimate the quantity and quality of each agricultural return flow based on the allocated water quantity and quality. In order to control the pollution loads of agricultural dischargers, it is assumed that a part of their return flows can be diverted to evaporation ponds. Results of applying the methodology to the Dez River system in the southwestern part of Iran show its effectiveness and applicability for simultaneous water and waste load allocation in rivers. It is shown that in our case study, the number of required optimizations in the fractional factorial analysis can be reduced from 64 to 16. Analysis of the interactive effects of uncertainties indicates that in a low flow condition, the upstream water quality would have a significant effect on the total benefit of the system.     

Monitoring of trace amounts of heavy metals in different food and water samples by flame atomic absorption spectrophotometer after preconcentration by amine-functionalized graphene nanosheet

We are introducing graphene oxide modified with amine groups as a new solid phase for extraction of heavy metal ions including cadmium(II), copper(II), nickel(II), zinc(II), and lead(II). Effects of pH value, flow rates, type, concentration, and volume of the eluent, breakthrough volume, and the effect of potentially interfering ions were studied. Under optimized conditions, the extraction efficiency is >97 %, the limit of detections are 0.03, 0.05, 0.2, 0.1, and 1 μg L^?1 for the ions of cadmium, copper, nickel, zinc, and lead, respectively, and the adsorption capacities for these ions are 178, 142, 110, 125, and 210 mg g^?1. The amino-functionalized graphene oxide was characterized by thermogravimetric analysis, transmission electron microscopy, scanning electron microscopy, and Fourier transform infrared spectrometry. The proposed method was successfully applied in the analysis of environmental water and food samples. Good spiked recoveries over the range of 95.8–100.0 % were obtained. This work not only proposes a useful method for sample preconcentration but also reveals the great potential of modified graphene as an excellent sorbent material in analytical processes.     

Rapid spectrophotometric determination of trace amounts of palladium in water samples after dispersive liquid–liquid microextraction

A simple, rapid, and efficient dispersive liquid–liquid microextraction method, followed by UV–Vis spectrophotometry was developed for the preconcentration and determination of Pd ions in water samples. Pd ions react with α-furildioxime (chelating agent) to form a hydrophobic complex. Various parameters were altered to study and optimize their effects on the extraction efficiency, such as pH, ligand concentration, the type and volume of extraction and dispersive solvents, extraction time, and salt concentration. Under optimized conditions, the method exhibited an enrichment factor (C _org/C _aq) of 25 and recovery more than 98 % within a very short extraction time. The linearity of the method ranged from 10 to 200 μg?L^?1. The limit of detection was 1.1 μg?L^?1. The relative standard deviation for the concentration of 100 μg?L^?1 of Pd was 2.3 % (n?=?10). Finally, the developed method was successfully applied to the extraction and determination of Pd in tap, river, mineral, and sea water samples.