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"Water quality management Mathematics."
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Mathematics Manual for Water and Wastewater Treatment Plant Operators - Three Volume Set
A comprehensive, self-contained mathematics reference, this three-volume set is useful to water and wastewater operators of all levels of expertise and experience. The three volumes cover fundamental math concepts, applied math concepts for water treatment operators, and applied math concepts for wastewater treatment operators. The texts highlight the kinds of exam questions operators can expect to see on state licensure examinations. Readers working through the volumes systematically will acquire a definitive understanding of performing applied water/wastewater calculations that are essential for a successful career in the water industry.
eBook
Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model
Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L) in the Small Prespa Lake in Greece, two standalone deep learning (DL) models, the long short-term memory (LSTM) and convolutional neural network (CNN) models, along with their hybrid, the CNN–LSTM model, were developed. The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models, support-vector regression (SVR) and decision tree (DT), were also developed to compare with the DL models. Time series of the physicochemical water quality variables, specifically pH, oxidation–reduction potential (ORP; mV), water temperature (°C), electrical conductivity (EC; µS/cm), DO and Chl-a, were obtained using a sensor at 15-min intervals from June 1, 2012 to May 31, 2013 for model development. Lag times of up to one (t − 1) and two (t − 2) for input variables pH, ORP, water temperature, and EC were used to predict DO and Chl-a concentrations, respectively. Each model’s performance in both training and testing phases was assessed using statistical metrics including the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), their normalized equivalents (RRMSE, RMAE; %), percentage of bias (PBIAS), Nash–Sutcliffe coefficient (ENS), Willmott’s Index, and graphical plots (Taylor diagram, box plot and spider diagram). Results showed that LSTM outperformed the CNN model for DO prediction, but the standalone DL models yielded similar performances for Chl-a prediction. Generally, the hybrid CNN–LSTM models outperformed the standalone models (LSTM, CNN, SVR and DT models) in predicting both DO and Chl-a. By integrating the LSTM and CNN models, the hybrid model successfully captured both the low and high levels of the water quality variables, particularly for the DO concentrations.
Journal Article
Mathematics for water and wastewater treatment plant operators
\"A comprehensive, self-contained mathematics reference, The Mathematics Manual for Water and Wastewater Treatment Plant Operators will be useful to operators of all levels of expertise and experience. The text is divided into three parts. Part 1 covers basic math, Part 2 covers applied math concepts, and Part 3 presents a comprehensive workbook with more than 1700 sample problems highlighting the kinds of exam questions operators can expect to see on state licensure examinations. Readers working through the book systematically will acquire a definitive understanding of and skill in performing the applied water/wastewater calculations that are essential for a successful career\"-- Provided by publisher.
Book
Impact of climate change on biodiversity loss: global evidence
The present study investigates the impact of climate change on biodiversity loss using global data consisting of 115 countries. In this study, we measure biodiversity loss using data on the total number of threatened species of amphibians, birds, fishes, mammals, mollusks, plants, and reptiles. The data were compiled from the Red List published by the International Union for Conservation of Nature (IUCN). For climate change variables, we have included temperature, precipitation, and the number of natural disaster occurrences. As for the control variable, we have considered governance indicator and the level of economic development. By employing ordinary least square with robust standard error and robust regression (M-estimation), our results suggest that all three climate change variables – temperature, precipitation, and the number of natural disasters occurrences – increase biodiversity loss. Higher economic development also impacted biodiversity loss positively. On the other hand, good governance such as the control of corruption, regulatory quality, and rule of law reduces biodiversity loss. Thus, practicing good governance, promoting conservation of the environment, and the control of greenhouse gasses would able to mitigate biodiversity loss.
Journal Article
Stream water quality prediction using boosted regression tree and random forest models
Reliable water quality prediction can improve environmental flow monitoring and the sustainability of the stream ecosystem. In this study, we compared two machine learning methods to predict water quality parameters, such as total nitrogen (TN), total phosphorus (TP), and turbidity (TUR), for 97 watersheds located in the Southeast Atlantic region of the USA. The modeling framework incorporates multiple climate and watershed variables (characteristics) that often control the water quality indicators in different landscapes. Three techniques, such as stepwise regression (SR), Least Absolute Shrinkage and Selection Operator (LASSO), and genetic algorithm (GA), are implemented to identify appropriate predictors out of 28 climate and catchment-related variables. The selected predictors were then used to develop the Random Forest (RF) and Boosted regression tree (BRT) models for water quality predictions in selected watersheds. The results highlighted that while both algorithms provided reasonable results (based on statistical metrics), the RF algorithm was easier to train and robust to model overfitting. Partial dependence plots highlighted the complex and nonlinear relationships between the individual predictors and the water quality indicators. The thresholds obtained from partial dependence plots showed that the median values of total nitrogen (TN) and total phosphorus (TP) in streams increase significantly when the percentage of urban and agricultural lands is above 40% and 43% of the watershed area, respectively. Furthermore, when soil hydraulic conductivity increases, the reduction in runoff results in decreased Turbidity levels in streams. Therefore, identifying the key watershed characteristics and their critical thresholds can help watershed managers create appropriate regulations for managing and sustaining healthy stream ecosystems. Besides, the forecasting models can improve water quality predictions in ungauged watersheds.
Journal Article
ESG performance, capital financing decisions, and audit quality: empirical evidence from Chinese state-owned enterprises
We study the nexus between environmental, social, and governance (ESG) performance and corporate capital financing decisions. Further, we also analyze the effect of audit quality and type of ownership (state-owned enterprises (SOEs) vs non-state-owned enterprises (non-SOEs), local vs central SOEs in this relationship. By applying panel regression (fixed effects) on 6295 firm-year observations of Chinese A-listed enterprises data for 2010–2019, we conclude that firms’ ESG information is crucial to their financing decisions. In particular, firms with superior ESG performance have lower debt financing. The findings suggest that enterprises with strong ESG performance have easy access to equity funding via stock markets. Further, this relationship is more pronounced in SOE compared to non-SOEs and in central SOEs compared to local SOEs. These results demonstrate that the market may promote desired social outcomes by rewarding ESG performance; however, we find no significant effect of audit quality in this relationship. Findings are robust to different sensitivity tests, including an alternative estimation, sysGMM regression to address endogeneity issues, and lagged regressions to address reverse causality.
Journal Article
International tourism, digital infrastructure, and CO2 emissions: fresh evidence from panel quantile regression approach
The main motivation behind this study is the importance of tourism and ICT industry in the economic development of a country and their potential effects on the country’s environmental quality in the digital era. For empirical analysis, the study applies FMOLS, DOLS, and quantile regression techniques for Asian economies. The findings of the study confirmed that tourism and digitalization improve environmental quality in FMOLS and DOLS models. In the basic quantile regression model, the estimates attached to tourism arrival are positive 5
th
quantile to 40
th
quantile and then turn negative from 60
th
quantile and onwards. Likewise, the estimates attached to tourism receipts in the robust quantile regression model are positive from quantile 5
th
to quantile 20
th
and negative and increasing from quantile 30
th
and onwards. Conversely, the estimates of digital infrastructure are insignificant in the basic quantile model at all quantiles except the 95
th
. However, the estimated coefficients of digital infrastructure in the robust model are negative and rising from 40
th
quantile to 70
th
quantile and negative and declining from 80
th
quantile to 95
th
quantile. In general, we can say that as the tourism and digital sectors grow, the CO
2
emissions decline.
Journal Article
Clean energy investment and financial development as determinants of environment and sustainable economic growth: evidence from China
Environmental sustainability has become one of the most common phrases in discussions about climate change. This study examines the impact of clean energy investment and financial development on environmental sustainability and China’s economic growth, using manufacturing value-added and urbanization as moderator variables from 1970 to 2016. We used advanced econometric methodologies for empirical estimations, used structural break unit root tests, fully modified least square, dynamic least square, and robust least square multiple regressions for long-run estimates. Overall, the results determine that clean energy investment is negatively associated with CO
2
emissions and ecological footprint while positively associated with China’s economic growth. Financial development, manufacturing value-added, and urbanization are positively associated with CO
2
emissions, ecological footprint, and China’s economic growth. Moreover, clean energy investment improves environmental sustainability at the expense of economic growth. Financial development, manufacturing value-added, and urbanization encourage economic growth at the expense of environmental sustainability. We argued that the local governments play a critical role in lifting the outstanding barriers to cleaner energy investment, addressing disincentives, including pricing carbon dioxide emissions, reforming inefficient nonrenewable fossil fuel subsidies, and addressing regulatory and market rigidities that can undesirably affect the attractiveness of clean energy investment. Policymakers are suggested to encourage green finance strategy for the financial sector to broader sustainable development objectives. At the heart of green manufacturing, industrialization policies are needed to integrate diverse intentions, like inclusive growth, environmental protection, and productivity through a wider range of economic, social, and environmental policy frameworks suitable for decoupling growth from social and environmental unsustainability.
Journal Article