River water quality assessment is one of the most important tasks to enhance water resources management plans. A water quality index (WQI) considers several water quality variables simultaneously. ...Traditionally WQI calculations consume time and are often fraught with errors during derivations of sub-indices. In this study, 4 standalone (random forest (RF), M5P, random tree (RT), and reduced error pruning tree (REPT)) and 12 hybrid data-mining algorithms (combinations of standalones with bagging (BA), CV parameter selection (CVPS) and randomizable filtered classification (RFC)) were used to create Iran WQI (IRWQIsc) predictions. Six years (2012 to 2018) of monthly data from two water quality monitoring stations within the Talar catchment were compiled. Using Pearson correlation coefficients, 10 different input combinations were constructed. The data were divided into two groups (ratio 70:30) for model building (training dataset) and model validation (testing dataset) using a 10-fold cross-validation technique. The models were evaluated using several statistical and visual evaluation metrics. Result show that fecal coliform (FC) and total solids (TS) had the greatest and least effect on the prediction of IRWQIsc. The best input combinations varied among the algorithms; generally variables with very low correlations displayed weaker performance. Hybrid algorithms improved the prediction power of several of the standalone models, but not all. Hybrid BA-RT outperformed the other models (R2 = 0.941, RMSE = 2.71, MAE = 1.87, NSE = 0.941, PBIAS = 0.500). PBIAS indicated that all algorithms, with the exceptions of RT, BA-RT and CVPS-REPT, overestimated WQI values.
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•16 novel hybrid data mining algorithm applied for WQI prediction•BA-RT algorithm outperformed while RFC-RT has the lowest prediction power.•Fecal coliform was the most effective predictor on WQI estimation.•The best input combination is not the same for all models.
Introduction: Due to various components, materials, and processes, industrial indoor air quality differs from building indoor air. Air quality and the working environment impact health, performance, ...and comfort. This study developed an Indoor Work Environmental Air Quality Index (IWEAQI) to assess and characterize industrial work environments.
Materials and methods: Surat “Textile city” is situated in the western part of India in Gujarat state. The small-scale dyeing and printing industry has been selected as a study area. The industry locations like Jet dyeing machine area, stenter machine area, printing machine area, looping machine area and washing basin area has been selected. Various chemicals, adhesives, solvents, dyes, and varied temperature and humidity conditions are used to transform the raw cloth into the finished product. CO, CO2, SO2, NO2, O3, Total Volatile Organic compounds (TVOC), Formaldehyde, Particulate Matters (PM10, PM2.5), WBGT index, humidity, noise, and light were considered to construct IWEAQI. Continuous observations were recorded at minute intervals with a real-time monitoring system. To account for all contributing aspects, United States Environmental Protection Agency (USEPA) air quality index technique was updated for index formulation. IWEAQI was validated using the Pollution Index approach.
Results: The proposed approach calculated IWEAQI from results. Both approaches gave an index value of 46-80. The developed approach and pollution index method were compared using regression analysis. All study locations had regression values between 0.93 and 0.99.
Conclusion: The technique classifies IWEAQI as excellent (0-20), good (21-40), moderate (41-60), poor (61-80), and very poor (81-100). From the developed index value, which parameters are influencing the most can be judged.
Air Quality Index is the simplest and widely used measure of overall air pollution of a region. Air Quality Assessment can be done with the help of Air Quality Index Parameter. Essentially it is used ...for assessing the air pollution hot spots in the region for delineating management and concrete actions. Different Methodology are adopted to assess the Quality of air for study area by taking into consideration of four pollutants synergistic effects which are PM10, PM2.5, SO2 and NO2 The average concentration recorded for PM10, PM2.5, SO2 and NO2 was recorded for six consecutive months before and after the lockdown period i.e., from 25th December 2019 till 25th June 2020. Different AQI were estimated for various months and varying results were observed ranging from good to unacceptable for the set of data. The findings of this study will provide detailed information of air pollution and AQI status before and after lockdown.
Despite a recent ambitious plan to improve waste management in Thailand, few studies have monitored the impact of these policies on beached marine litter. Here, we assessed weekly the amounts and ...composition of stranded macro-litter (≥2.5 cm) on five beaches from an uninhabited island in Thailand during one year. A total of 24,407 items (391.86 kg) yielded a mean abundance of 3.18 ± 11.39 items m−2 (52.75 ± 204.68 g m−2), with plastic being the most abundant marine litter (48% of the total number). The overall Clean Coast Index (30.1) classified the beaches as ‘extremely dirty’, with a Plastic Abundance Index of 9.8 (‘very high abundance’ of plastics). When assessing the seasonal rates of accumulation, we found a higher flux pre-monsoon (0.05 items m−2 d−1; 0.66 g m−2 d−1) than post-monsoon (0.01 items m−2 d−1; 0.35 g m−2 d−1). Using modeling of the local hydrodynamic conditions, we explored the potential sources of the pollution, and surprisingly found that the closest river appeared not to be the source. Our results denote that the distribution and typology of marine litter were representatives of household and fishing activities, which in turn highlights the need for better regional litter management measures.
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•●Site location and season influenced macro-litter distribution over 7 months.•Plastic was the dominant debris, with a prevalent contribution of single-use items.•Marine litter were representatives of household and fishing activities.•Modeling of currents suggests that litter did not originate from the closest river.•Despite a recent management plan, Thailand is still challenged by marine pollution.
•Twenty-one different WQI models were identified and reviewed.•Rivers are by far the most common application of WQI models.•Most models comprised of four key components, the specifics of which varied ...significantly.•Uncertainty and eclipsing problems are key issues affecting model accuracy.
The water quality index (WQI) model is a popular tool for evaluating surface water quality. It uses aggregation techniques that allow conversion of extensive water quality data into a single value or index. Globally, the WQI model has been applied to evaluate water quality (surface water and groundwater) based on local water quality criteria. Since its development in the 1960s, it has become a popular tool due to its generalised structure and ease-of-use. Commonly, WQI models involve four consecutive stages; these are (1) selection of the water quality parameters, (2) generation of sub-indices for each parameter (3) calculation of the parameter weighting values, and (4) aggregation of sub-indices to compute the overall water quality index. Several researchers have utilized a range of applications of WQI models to evaluate the water quality of rivers, lakes, reservoirs, and estuaries. Some problems of the WQI model are that they are usually developed based on site-specific guidelines for a particular region, and are therefore not generic. Moreover, they produce uncertainty in the conversion of large amounts of water quality data into a single index.
This paper presents a comparative discussion of the most commonly used WQI models, including the different model structures, components, and applications. Particular focus is placed on parameterization of the models, the techniques used to determine the sub-indices, parameter weighting values, index aggregation functions and the sources of uncertainty. Issues affecting model accuracy are also discussed.
Many urban water bodies grapple with low flow flux and weak hydrodynamics. To address these issues, projects have been implemented to form integrated urban water bodies via interconnecting artificial ...lake or ponds with rivers, but causing pollution accumulation downstream and eutrophication. Despite it is crucial to assess eutrophication, research on this topic in urban interconnected water bodies is limited, particularly regarding variability and feasible strategies for remediation. This study focused on the Loucun river in Shenzhen, comprising an pond, river and artificial lake, evaluating water quality changes pre-(post-)ecological remediation and establishing a new method for evaluating the water quality index (WQI). The underwater forest project, involving basement improvement, vegetation restoration, and aquatic augmentation, in the artificial lake significantly reduced total nitrogen (by 43.58%), total phosphorus (by 79.17%) and algae density (by 36.90%) compared to pre-remediation, effectively controlling algal bloom. Rainfall, acting as a variable factor, exacerbated downstream nutrient accumulation, increasing total phosphorus by 4.56 times and ammonia nitrogen by 1.30 times compared to the dry season, and leading to algal blooms in the non-restoration pond. The improved WQI method effectively assesses water quality status. The interconnected water body exhibits obvious nutrient accumulation in downstream regions. A combined strategy that reducing nutrient and augmenting flux was verified to alleviate accumulation of nutrients downstream. This study provides valuable insights into pollution management strategies for interconnected pond-river-lake water bodies, offering significant reference for nutrient mitigation in such urban water bodies.
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•Ecological restoration effectively reduces nitrogen, phosphorus, and algae density.•Rainfall is an adverse factor exacerbating nutrient load post-restoration.•An improved water quality index based on algae correlation weight was developed.•Enhancing upstream flux and reducing nutrient sources aid in nutrient mitigation.
The Sürgü Stream, located in the Euphrates River basin of Turkey, is used for drinking water source, agricultural irrigation and rainbow trout production. Therefore, water quality of the stream is of ...great importance. In this study, multivariate statistical techniques (MSTs) and water quality index (WQI) were applied to assess water quality of the stream affected by multiple stressors such as untreated domestic sewage, effluents from fish farms, agricultural runoff and streambank erosion. For this, 16 water quality parameters at five sites along the stream were monitored monthly during one year. Most of parameters showed significant spatial variations, indicating the influence of anthropogenic activities. All parameters except TN (total nitrogen) showed significant seasonal differences due to high seasonality in WT (water temperature) and water flow. The spatial variations in the WQI were significant (p < 0.05) and the mean WQI values ranged from 87.6 to 95.3, indicating “good” to “excellent” water quality in the stream. Cluster analysis classified five sites into three groups, that is, clean region, low polluted region and very clean region. Stepwise temporal discriminant analysis (DA) identified that pH, WT, Cl−, SO42−, COD (chemical oxygen demand), TSS (total suspended solids) and Ca2+ are the parameters responsible for variations between seasons, and stepwise spatial DA identified that DO (dissolved oxygen), EC (electrical conductivity), NH4–N, TN (total nitrogen) and TSS are the parameters responsible for variations between the regions. Principal component analysis/factor analysis revealed that the parameters responsible for water quality variations were mainly associated with suspended solids (both natural and anthropogenic), soluble salts (natural) and nutrients and organic matter (anthropogenic).
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•Effect of multiple stressors on stream water quality was investigated.•WQI was applied to determine the water quality status of the stream.•Multivariate statistical techniques were used for interpretation of data set.•Flow and temperature caused high seasonality in most of variables.
Effect of multiple stressors on stream water quality was investigated using multivariate statistical techniques and water quality index.
•Water quality indices are useful for indicating total effect of ecological factors.•The results can help local people in improving water quality of Al-Gharraf River.•Al-Gharraf water is unpotable ...(67.3 WQI) due to natural and anthropogenic factors.•Al-Gharraf River water is poor for aquatic life but fair for irrigation.
The Water Quality Index has been developed mathematically to evaluate the water quality of Al-Gharraf River, the main branch of the Tigris River in the south of Iraq. Water samples were collected monthly from five sampling stations during 2015–2016, and 11 parameters were analyzed: biological oxygen demand, total dissolved solids, the concentration of hydrogen ions, dissolved oxygen, turbidity, phosphates, nitrates, chlorides, as well as turbidity, total hardness, electrical conductivity and alkalinity. The index classified the river water, without including turbidity as a parameter, as good for drinking at the first station, poor at stations 2, 3, 4 and very poor at station 5. When turbidity was included, the index classified the river water as unsuitable for drinking purposes in the entire river. The study highlights the importance of applying the water quality indices which indicate the total effect of the ecological factors on surface water quality and which give a simple interpretation of the monitoring data to help local people in improving water quality.
Amid the COVID-19 pandemic, a nationwide lockdown is imposed in India initially for three weeks from 24th March to 14th April 2020 and extended up to 3rd May 2020. Due to the forced restrictions, ...pollution level in cities across the country drastically slowed down just within few days which magnetize discussions regarding lockdown to be the effectual alternative measures to be implemented for controlling air pollution. The present article eventually worked on this direction to look upon the air quality scenario amidst the lockdown period scientifically with special reference to the megacity Delhi. With the aid of air quality data of seven pollutant parameters (PM10, PM2.5, SO2, NO2, CO, O3 and NH3) for 34 monitoring stations spread over the megacity we have employed National Air Quality Index (NAQI) to show the spatial pattern of air quality in pre and during-lockdown phases. The results demonstrated that during lockdown air quality is significantly improved. Among the selected pollutants, concentrations of PM10 and PM2.5 have witnessed maximum reduction (>50%) in compare to the pre-lockdown phase. In compare to the last year (i.e. 2019) during the said time period the reduction of PM10 and PM2.5 is as high as about 60% and 39% respectively. Among other pollutants, NO2 (−52.68%) and CO (−30.35%) level have also reduced during-lockdown phase. About 40% to 50% improvement in air quality is identified just after four days of commencing lockdown. About 54%, 49%, 43%, 37% and 31% reduction in NAQI have been observed in Central, Eastern, Southern, Western and Northern parts of the megacity. Overall, the study is thought to be a useful supplement to the regulatory bodies since it showed the pollution source control can attenuate the air quality. Temporary such source control in a suitable time interval may heal the environment.
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•PM10 and PM2.5 concentrations reduced by about half in compare to the pre-lockdown•NO2 and CO have also shown considerable decline during lockdown.•In the transportation and industrial location air quality have improved close to 60%.•The central and Eastern Delhi have experienced maximum improvement in air quality.•On the 2nd and 4th day of lockdown, about 40% to 50% improvement in air quality
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