A New Method for Continuous Monitoring of Black and Odorous Water Body Using Evaluation Parameters: A Case Study in Baoding

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.

Carbon Monoxide Emission and Air Quality Analysis Based on an Improved Double-Weight Fuzzy Comprehensive Evaluation

In this article, we adopt an improved double-weighted fuzzy comprehensive evaluation method to investigate the air condition of Hefei City from July 2016 to July 2021. We focus on the impact of the toxicity index, especially the impact of carbon monoxide, which is also considered in some other kinds of quality evaluation, such as water classification. Firstly, we found that with the increasing awareness of environmental protection and with the attention of the government to the quality of air in recent years, the air conditions have become better (the grades become lower). Secondly, the value of the factors, PM2.5, PM10, SO2, CO, NO2, and O3 periodically fluctuate from year to year; and the periodicity of O3 is reversed with the other factors. Finally, the monthly average analysis shows that the overall air quality is good; all the grades are I-II, except for December 2017 which has a grade III. Furthermore, the air quality in the winter (especially in December and January) is not always good.

Multi-Temporal Surface Water Classification for Four Major Rivers from the Peruvian Amazon

We describe a new minimum extent, persistent surface water classification for reaches of four major rivers in the Peruvian Amazon (i.e., Amazon, Napo, Pastaza, Ucayali). These data were generated by the Peruvian Amazon Rural Livelihoods and Poverty (PARLAP) Project which aims to better understand the nexus between livelihoods (e.g., fishing, agriculture, forest use, trade), poverty, and conservation in the Peruvian Amazon over a 35,000 km river network. Previous surface water datasets do not adequately capture the temporal changes in the course of the rivers, nor discriminate between primary main channel and non-main channel (e.g., oxbow lakes) water. We generated the surface water classifications in Google Earth Engine from Landsat TM 5, 7 ETM+, and 8 OLI satellite imagery for time periods from circa 1989, 2000, and 2015 using a hierarchical logical binary classification predominantly based on a modified Normalized Difference Water Index (mNDWI) and shortwave infrared surface reflectance. We included surface reflectance in the blue band and brightness temperature to minimize misclassification. High accuracies were achieved for all time periods (>90%).

Study on Monitoring Water Area in Irrigation Area by Local Space Self-correlation Index

Landsat remote sensing image is a widely used data source in water remote sensing. Normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) are commonly used water extraction classifiers. In the process of their application, because the threshold varies with the location and time of the research object, how to select the threshold with the highest classification accuracy is a time-consuming and challenging task. The purpose of this study was to explore a method that can not only improve the accuracy of water extraction, but also provide a fixed threshold, and can meet the requirements of automatic water extraction. We introduced the local spatial auto correlation statistics and calculate the Getis-Ord Gi* index to have hot spot analysis. Comparative analysis showed that the accuracy of water classification had been greatly improved through hot spot analysis. AWEIsh classifier had the best classification accuracy under the condition of INVERSE_DISTANCE neighborhood rule and Z>1.96, and the accuracy changes least in different time, different location and different vegetation coverage images. Therefore, in the process of regional water extraction, hot spot analysis method was effective, which was helpful to improve the accuracy of water extraction.

Mineral water mining: quality for human consumption and health promotion

This study demonstrates the aspects of research and mining in groundwater mining. It was intended to demonstrate the guidelines of the Mining Code and Water Code as legal beacons under the management of the National Mining Agency – ANM, federal regulator of mineral water exploration; to analyze the importance of water quality control for human consumption, from the compliance with Anvisa Resolutions – RDC-274/275/2005, which standardize the vase and microbiological characteristics. The process to make groundwater available as a mineral good to be industrialized, goes through research stages, till the final test with laboratory analyses for water classification. In this context, its main question is: what is the importance of mineral water quality for human consumption in health promotion? And it aims to analyze the quality control of mineral water, its importance for human consumption and health promotion, as well as the benefits of it. For this, a bibliographic research was carried out, consultation with the relevant legislations, books and websites of public agencies. The results showed that mineral water mining is an activity present in the daily life of society and its regular consumption contributes to the quality and promotion of healthy living, through control of the exploitation of mineral water mines, under the management of the National Mining Agency – ANM. It was evidenced that mineral water brings important health benefits: in body thermoregulation, heat absorption, nutrient transport, oxygen, hormones, composition of blood plasma, digestive juice, saliva formation, tears and urine. Being composed of different levels of micronutrients, which help alleviating health problems, such as: weakening of bones and muscles, reduction of collagen, dryness of the skin, low revitalization of cells and mucosa, aggravated by low consumption of water with drug properties and product of secular use that affect health. It was concluded that the prevalence of each type of mineral water and its composition, from ferruginous, sulfurous, radioactive, magnesian and iodized, can help in therapeutic treatments.

Geochemical Evaluation of Formation Water, Mauddud Reservoir, Khabbaz Oilfield, Kirkuk Area, Northern Iraq

In this study, formation-water samples were collected by NOC Staff, during drilling time, from the Mauddud Formation reservoir of the Khabbaz Oilfield, for this reason four samples from four wells; Kz-3, Kz-4, Kz-7, and Kz-23 were selected to geochemical analysis. Analyzed geochemical parameters include TDS and the concentrations of the different dissolved cations and anions present in brines (Ca+2, Mg+2, Na+1, SO4-2, Cl-1, HCO3-1, and NaCl). Variations among the resulted data are discussed by comparison with other Cretaceous Brines. Geochemical ratios of Na/Cl, (Na-Cl)/SO4) and (Cl-Na)/Mg+2 was calculated for formation water classification following Bojarski, (1970). The calculated geochemical ratios of the studied samples in the studied four wells indicate that all of these waters are "chloride calcium" type under subsurface conditions, this type reflect closed system isolated associations reservoir, which are becoming high hydrostatic in deeper zones without influence by infiltration waters. A major transversal fault cutting the structure at its SE plunge had participated in the dilution of the Mauddud reservoir brine effectively.

Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions

This study presents an automated methodology to generate training data for surface water mapping from a single Sentinel-2 granule at 10 m (4 band, VIS/NIR) or 20 m (9 band, VIS/NIR/SWIR) resolution without the need for ancillary training data layers. The 20 m method incorporates an ensemble of three spectral indexes with optimal band thresholds, whereas the 10 m method achieves similar results using fewer bands and a single spectral index. A spectrally balanced and randomly generated set of training data based on the index values and optimal thresholds is used to fit machine learning classifiers. Statistical validation compares the 20 m ensemble-only method to the 20 m ensemble method with a random forest classifier. Results show the 20 m ensemble-only method had an overall accuracy of 89.5% (±1.7%), whereas the ensemble method combined with the random forest classifier performed better, with a ~4.8% higher overall accuracy: 20 m method (94.3% (±1.3%)) with optimal spectral index and SWIR thresholds of −0.03 and 800, respectively, and 10 m method (93.4% (±1.5%)) with optimal spectral index and NIR thresholds of −0.01 and 800, respectively. Comparison of other supervised classifiers trained automatically with the framework typically resulted in less than 1% accuracy improvement compared with the random forest, suggesting that training data quality is more important than classifier type. This straightforward framework enables accurate surface water classification across diverse geographies, making it ideal for development into a decision support tool for water resource managers.

Spectral Probability Distribution of Closed Connected Water and Remote Sensing Statistical Inference for Yellow Substance

Unlike traditional remote sensing inversion, this study proposes a new distribution–distribution scheme, which uses statistical inferences to estimate the probability distribution of in-water components based on the probability distribution of the observed spectra. The distribution–distribution scheme has the advantages that it rapidly gives the statistical information of the water of interest, assists the traditional scheme in improving models, and provides more valuable information for water classification and aquatic environment analysis. In this study, based on Landsat-8 images, we analyzed the spectral probability distributions of 688 global waters and found that many of them were normal, log normal, and exponential distributions with diverse patterns in distribution parameters such as the mean, standard deviation, skewness, and kurtosis. Using simulated and field-measured data, we propose a bootstrap-based distribution–distribution scheme and develop some simple remote sensing statistical inference models to estimate the distribution parameters of yellow substance in water.

A Universal Fuzzy Logic Optical Water Type Scheme for the Global Oceans

The classification of natural waters is a way to generalize and systematize ocean color science. However, there is no consensus on an optimal water classification template in many contexts. In this study, we conducted an unsupervised classification of the PACE (Plankton, Aerosols, Cloud, and Ocean Ecosystem) synthetic hyperspectral data set, divided the global ocean waters into 15 classes, then obtained a set of fuzzy logic optical water type schemes (abbreviated as the U-OWT in this study) that were tailored for several multispectral satellite sensors, including SeaWiFS, MERIS, MODIS, OLI, VIIRS, MSI, and OLCI. The consistency analysis showed that the performance of U-OWT on different satellite sensors was comparable, and the sensitivity analysis demonstrated the U-OWT could resist a certain degree of input disturbance on remote sensing reflectance. Compared to existing ocean-aimed optical water type schemes, the U-OWT can distinguish more mesotrophic and eutrophic water classes. Furthermore, the U-OWT was highly compatible with other water classification taxonomies, including the trophic state index, the multivariate absorption combinations, and the Forel-Ule Scale, which indirectly demonstrated the potential for global applicability of the U-OWT. This finding was also helpful for the further conversion and unification of different water type taxonomies. As the fundamental basis, the U-OWT can be applied to many oceanic fields that need to be explored in the future. To promote the reproducibility of this study, an IDL®-based standalone U-OWT calculation tool is freely distributed.