Effluent guidelinesEffluent Guidelines (also referred to as Effluent Limitation Guidelines (ELGs)) are U.S. national standards for wastewater discharges to surface waters and publicly owned treatment works (POTW) (also called municipal sewage treatment plants). The United States Environmental Protection Agency (EPA) issues Effluent Guideline regulations for categories of industrial sources of water pollution under Title III of the Clean Water Act (CWA). The standards are technology-based, i.e.
Combined sewerA combined sewer is a type of gravity sewer with a system of pipes, tunnels, pump stations etc. to transport sewage and urban runoff together to a sewage treatment plant or disposal site. This means that during rain events, the sewage gets diluted, resulting in higher flowrates at the treatment site. Uncontaminated stormwater simply dilutes sewage, but runoff may dissolve or suspend virtually anything it contacts on roofs, streets, and storage yards.
Lake stratificationLake stratification is the tendency of lakes to form separate and distinct thermal layers during warm weather. Typically stratified lakes show three distinct layers: the epilimnion, comprising the top warm layer; the thermocline (or metalimnion), the middle layer, whose depth may change throughout the day; and the colder hypolimnion, extending to the floor of the lake. Every lake has a set mixing regime that is influenced by lake morphometry and environmental conditions.
GreywaterGreywater (or grey water, sullage, also spelled gray water in the United States) refers to domestic wastewater generated in households or office buildings from streams without fecal contamination, i.e., all streams except for the wastewater from toilets. Sources of greywater include sinks, showers, baths, washing machines or dishwashers. As greywater contains fewer pathogens than blackwater, it is generally safer to handle and easier to treat and reuse onsite for toilet flushing, landscape or crop irrigation, and other non-potable uses.
CorrelationIn statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.
Pearson correlation coefficientIn statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations.
Groundwater pollutionGroundwater pollution (also called groundwater contamination) occurs when pollutants are released to the ground and make their way into groundwater. This type of water pollution can also occur naturally due to the presence of a minor and unwanted constituent, contaminant, or impurity in the groundwater, in which case it is more likely referred to as contamination rather than pollution.
Correlation coefficientA correlation coefficient is a numerical measure of some type of correlation, meaning a statistical relationship between two variables. The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution. Several types of correlation coefficient exist, each with their own definition and own range of usability and characteristics. They all assume values in the range from −1 to +1, where ±1 indicates the strongest possible agreement and 0 the strongest possible disagreement.
Linear regressionIn statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Coefficient of multiple correlationIn statistics, the coefficient of multiple correlation is a measure of how well a given variable can be predicted using a linear function of a set of other variables. It is the correlation between the variable's values and the best predictions that can be computed linearly from the predictive variables. The coefficient of multiple correlation takes values between 0 and 1.
