Coffee is the second-most consumed beverage in the world, and its high demand is covered by countries such as Peru, where the waste generated in production causes environmental pollution. We sought to determine the concentration of alcohol components and other volatiles compounds present in the distillate, after fermenting wastewater from the first wash in the wet processing of Coffea arabica var. catimor under five conditions: C1 (pasteurized + 0.325 g sucrose + 8.000 g S. cerevisiae), C2 = (pasteurized + 8.000 g S. cerevisiae), C3 (0.325 g sucrose + 8.000 g S. cerevisiae), C4 = (8.000 g S. cerevisiae), and C5 = (natural state). The solid-phase microextraction technique was used to determine the composition of the distillates by gas chromatography (GC). 35 components were detected, 11 of them under all conditions. Ethanol was the most abundant element in all five fermentation conditions. Condition 1 shows the highest value at 97.29 μg.mL-1, though all five concentrations can be considered high. This study shows that wastewater from the wet processing of coffee can have agro-industrial use as a value-added product. Postharvest Peruvian coffee is amenable to strategies aligned with the sustainable development goal of reducing food losses along production and supply chains.

In this study, an alternative approach was applied for the characterization of the subsurface geological conditions to estimate the hydrological parameters in the absence of subsurface soil data. The study revealed that the hydrological parameters, estimated from the Transient Electromagnetic (TEM) and Electrical Resistivity Tomography (ERT), were significantly correlated with in situ data. Overall estimated infiltration rate (below 20 inches/h) predicted fine-grained soil was also associated with in situ data. A high correlation among the bulk electrical resistivity, porosity, and the resistivity of the pore fluid thereby confirmed the relevance of Archie’s law used in this study. Furthermore, results showed that both TEM and ERT are vital tools for hydrological parameter estimation.

The objective of this study is to assess the air quality and water quality at Madurai city using the spatial interpolation technique of the Geographic Information System (GIS). Inverse Distance Weighting (IDW), Kriging, Gaussian- Kriging methods of interpolation are used in the study. The study area is divided into 8 zones and primary data is collected through direct sampling for water. The air pollution concentration data on Suspended Particulate Matter (SPM), Respirable Particulate Matter (RPM), and Lead are collected from an air quality monitoring network system of Tamilnadu Pollution Control Board (TNPCB). The results indicate that the Kriging interpolation method performs well with an error value of 0.02. The data collected from the years 2006 and 2020 are compared using GIS and observed data. The comparison of results indicates that there is a good accord with the predicted value using Inverse Distance Weighting (IDW) and Kriging interpolation with the observed data. The analysis report indicates that the pollution level at all 8 blocks was severely affected in 2020 and it is above the prescribed standard of TNPCB. Particularly, Kalavasal, Arapalyam, and Periyar blocks are severely affected. This work also presents suitable mitigation measures to reduce pollution.

Henan is China’s most populous agricultural province. Wheat, cotton, oil crops, and other farming and animal husbandry industries rank first in China in terms of output value. The agricultural sector has grown significantly, and rural populations’ living standards have greatly improved. The effects of rural urbanization can be seen all over the place. However, the rural economy’s rapid growth has resulted in a slew of environmental contamination issues. Chemical fertilizer emissions of chemical oxygen demand (COD) and total nitrogen (TN) have been decreasing for a long time in Henan Province, whereas total phosphorus (TP) emissions are increasing. The usage load of pesticides went high in Zhoukou, Nanyang, Shangqiu, Xinyang, Xinxiang, and Zhumadian. The application of agricultural film was relatively common in Nanyang, Zhoukou, Anyang, Xinyang, Zhumadian, Kaifeng, and Shangqiu. Wheat, corn, and oil-bearing crop stalks are the main sources of crop solid waste in Henan Province, and the solid waste generated by these three crops accounted for 93.96% of the province’s total amount. In Zhumadian, Zhoukou, Nanyang, and Shangqiu, there was a lot of crop stalk trash.

Organic acids are widespread in the environment and play an important role in the adsorption, desorption, and migration of soil cadmium (Cd). Under the effect of humic acid (HA) and citric acid, the properties and mechanism of Cd adsorption and desorption in reclaimed soil from the Panyi Mine in the Huainan mining area were investigated (CA). The addition of HA and CA inhibited the adsorption and enhanced the desorption of Cd in reclaimed soil, with HA having a stronger effect than CA. As the concentration of added HA or CA increased, the adsorption capacity of Cd in the reclaimed soil gradually decreased, while the desorption capacity gradually increased. That is, the adsorption inhibition/desorption promotion effect was stronger for higher concentrations of organic acid. Cd adsorption and desorption could be described as a logarithmic function of organic acid concentration. The kinetic curves of Cd adsorption and desorption in reclaimed soil under the influence of organic acids show that both adsorption and desorption involved two stages: a fast reaction stage followed by a slow reaction stage.

This work studied the characteristics of leaf, rice straw, and sewage sludge (SS) co-composting with the aim of determining the best composting ratio by monitoring temperature changes, oxygen (O2) concentration, carbon dioxide (CO2) concentration, ammonia (NH3) concentration, hydrogen sulfide (H2S) concentration, pH, electrical conductivity (EC), heavy metal content, carbon-nitrogen ratio (C/N ratio), germination index (GI), moisture content (MC), and volatile solids (VS) content during the composting process. Three composting piles with the mixture ratios of 4:1:1 (Pile A), 5:1:1 (Pile B), and 6:1:1(Pile C) (SS: leaf: rice straw) were tested. According to the temperature, C/N ratio, germination index, MC, and VS, the level of compost maturity in Pile B with a 5:1:1 mixing ratio was higher than that in Piles A and C. The contents of heavy metals in the composts were shown to meet the grade A standard in CJ/T 309-2009 (2009), except Cu and Zn, which was within the grade B standard.

The pollution status of surface sediments in the Luhun drinking water reservoir in the central of China was analyzed and evaluated, and three sampling points were selected for this study. The results showed that the organic matter content of the Luhun reservoir was as high as 5.2%, which was at a high level. The analysis of nitrogen, phosphorus, and their components showed that the total nitrogen (TN) and total phosphorus (TP) pollution in Luhun reservoir was in the medium pollution level, among which the ion-exchange state of nitrogen component and the strong-alkali extraction state of phosphorus component accounted for a relatively high risk of release into the overlying water. The results showed that the bioavailability index of Pb and Cr was as high as 0.73 and 0.62, which was of big pollution risk. Generally speaking, the sediment of Luhun reservoir had a high risk of pollution to the overlying water. The results of this study can provide a theoretical basis for urban safe water supply and provide support for water quality improvement.

Typhoons are the main cause of disturbances in the natural environment of coastal cities. Typhoons often damage the urban green space (UGS) of coastal cities, and the spatial pattern of such damages provides important information for disaster recovery. For acquiring such information, remote sensing technology offers a fast and effective means. This study investigated the spatial pattern of typhoon damage to UGS using Sentinel-2 data. To this end, the damage caused by Typhoon Meranti to the UGS of Xiamen Island (including Gulangyu Island) in 2016 was taken as a case study. The results showed that the overall area without vegetation coverage increased by 1159.7 ha. Areas with high vegetation coverage experienced less damage than areas with low vegetation coverage. A coldspot map of damage clusters was generated, and the map showed that severely damaged green areas were distributed in a striped pattern, indicating serious damage to road greening. In terms of direction, east-west roads experienced a higher degree of damage than north-south roads. The determined spatial pattern of the damage caused by Typhoon Meranti on the UGS of Xiamen Island provides a basis for the post-disaster restoration of the landscape of Xiamen Island.

A study and spatiotemporal radiological characterization of water for human use and consumption in the main rural populations of the center of the State of Veracruz was carried out, covering 22 municipalities. The objective was to estimate the annual effective dose as a function of the concentration of gross alpha and beta activity. For this purpose, a low background proportional flux detection system calibrated with NIST-traceable radioactive standards was used. Sampling included only wells, springs, and lagoons in rural areas that supply water to these populations. The decision was based on the fact that these do not have a physicochemical treatment and was carried out during the dry and rainy seasons, which became factors of impact on the radiological material. The analysis included the results of 195 samples from 22 municipalities which showed ranges in the gross alpha of 0.052-0.95 BqL-1 with a mean of 0.376 ±0.101BqL-1 and a gross beta of 0.034-1.48 BqL-1 with a mean of 0.389 ±0.108 BqL-1. The comparison of the values obtained with respect to those of other countries and their complement with analysis of variance showed that there was a significant difference, particularly, for the results of gross alpha in the municipality of Alto Lucero de Gutierrez Barrios and gross beta in Nautla and Tecolutla in dry and rainy seasons (at a probability of p≤0.05 with the Tukey-Kramer HSD statistical test). A correlation between gross alpha and gross beta was also performed with an r of -0.18 and -0.44 in dry and rainy seasons. This means that among the radionuclides, the major sources of beta radiation are uranium and thorium decay series radionuclides. For the determination of gross alpha, the municipalities in the mountainous zones showed lower values of this activity than the municipalities in the coastal zone. Gross alpha activity values of 0.95 ± 0.11 BqL-1 were detected in the municipality of Alto Lucero de Gutiérrez Barrios in the locality of Arroyo Agrio, which exceeded the limit of the Official Mexican Standard.

The use of deep learning methods to detect plant diseases and pests based on UAV images is an important application of remote sensing technology in modern forestry. This paper uses a CenterNet-based object detection method to construct models for plant disease and pest detection. The accuracy of the models is influenced by parameter alpha, which is used to control the affine transformation in the preprocessing of CenterNet. First, different alphas are sampled for training and testing. Next, the least square method is used to fit the curve between alpha and accuracy measured by mAP (mean average precision). Finally, the equation of the curve is fitted as mAP = -0.22 * alpha2 + 0.32 * alpha + 0.42. In comparison, an automated machine learning (AutoML) method is also conducted to automatically search for the best model. The experiments are done with 5,281 images as the training dataset, 1,319 images as the verification dataset, and 3,842 images as the test dataset. The results show that the best alpha value obtained by the least square method is 0.733, and the accuracy of the corresponding model is 0.536 in mAP@[.5, .95]. In contrast, the accuracy of the AutoML method model is higher with the model accuracy of 0.545 in mAP@[.5, .95]. However, the training time and training resource consumption of the AutoML method are about 3 times that of the least square method. Therefore, in practice, a trade-off should be made according to the accuracy requirements, resource consumption, and task urgency.