Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing

The water components affecting turbidity are complex and changeable, and the spectral response mechanism of each water quality parameter is different. Therefore, this study mainly aimed at the turbidity monitoring by unmanned aerial vehicle (UAV) hyperspectral technology, and establishes a set of turbidity retrieval models through the artificial control experiment, and verifies the model’s accuracy through UAV flight and water sample data in the same period. The results of this experiment can also be extended to different inland waters for turbidity retrieval. Retrieval of turbidity values of small inland water bodies can provide support for the study of the degree of water pollution. We collected the images and data of aquaculture ponds and irrigation ditches in Dawa District, Panjin City, Liaoning Province. Twenty-nine standard turbidity solutions with different concentration gradients (concentration from 0 to 360 NTU—the abbreviation of Nephelometric Turbidity Unit, which stands for scattered turbidity.) were established through manual control and we simultaneously collected hyperspectral data from the spectral values of standard solutions. The sensitive band to turbidity was obtained after analyzing the spectral information. We established four kinds of retrieval, including the single band, band ratio, normalized ratio, and the partial least squares (PLS) models. We selected the two models with the highest R2 for accuracy verification. The band ratio model and PLS model had the highest accuracy, and R2 was, respectively, 0.65 and 0.72. The hyperspectral image data obtained by UAV were combined with the PLS model, which had the highest R2 to estimate the spatial distribution of water turbidity. The turbidity of the water areas in the study area was 5–300 NTU, and most of which are 5–80 NTU. It shows that the PLS models can retrieve the turbidity with high accuracy of aquaculture ponds, irrigation canals, and reservoirs in Dawa District of Panjin City, Liaoning Province. The experimental results are consistent with the conclusions of the field investigation.

A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile

Lake surface water temperature (LSWT) is a crucial water quality parameter that modulates many lake and reservoir processes. Therefore, it is necessary to monitor it from a long-term perspective. Over the last decades, many methods to retrieve LSWT fields from satellite imagery have been developed. This work aims to test, implement and automate six methods. These are performed in the Google Earth Engine (GEE) platform, using 30 m spatial resolution images from Landsat 7 and 8 satellites for 2000–2020. Automated methods deliver long-term time series. Series are then calibrated with in situ data. Two-dimensional (2D) × time data fields are built on the lakes with the calibration, and a subsequent LSWT climatology is derived. Our study area is two urban lagoons with areas smaller than two (2) km2 of the city of San Pedro de la Paz, South-Central Chile. The six methods describe the seasonal variation of LSWT (Willmott’s index of agreement > 0.91, R2 > 0.67). The main difference between series is their bias. Thus, after a simple calibration, all series adequately describe the LSWT. We utilized the Pedro de la Paz lagoons to demonstrate the method’s utility. Our research demonstrates that these adjacent lagoons exhibit comparable LSWT spatial (15.5–17 ∘C) and temporal (7–25 ∘C) trends throughout the year. Differences in geographical pattern might result from the northern island’s heat impact and the existence of the Biobío river to the east. Our work represents an efficient alternative for obtaining LSWT in particular lakes and reservoirs, especially useful in medium and small-sized ones.

Phytoremediation in Sandpit Lake: Aquaponic

Sandpit Lake in Sukabumi area is a suitable site for developing aquaculture. However due to rapid eutrophication only sustainable aquaculture should be applied. We evaluated the ability of lettuce in reducing nutrients from fish culture. This phytoremediation study was carried out using mesocosm, where lettuce as the phytoremediation agent were grown on vegetable floating raft (VFR) together with 25 fish which were grown underneath it and fed by fish feed. We built three sizes of VFR which were 20, 40 and 60% covered the mesocosm surface. The three sizes VFR were the treatment and the 0% covering served as control. Water quality sampling was carried out two times at day zero and day seven of the experiment. We collected temperature, oxygen, nitrogen, and phosphate data and analyzed them using ANOVA. Our result showed the nitrogen increased inside mesocosm meanwhile orthoposphat decreased. But the size of VFR did not give any effect to the water quality parameter change. However descriptively, we found that 40% of VFR was able to slowdown the nutrien TAN increasing and orthoposphat concentration found to be low. Moreover, it resulted in the highest lettuce survival. We recommend using 40% coverage in employing an aquaponic system in sandpit lake.

Water Quality Assessment and Potential Source Contribution Using Multivariate Statistical Techniques in Jinwi River Watershed, South Korea

To investigate the effects of rapid urbanization on water pollution, the water quality, daily unit area pollutant load, water quality score, and real-time water quality index for the Jinwi River watershed were assessed. The contribution of known pollution sources was identified using multivariate statistical analysis and absolute principal component score-multiple linear regression. The water quality data were collected during the dry and wet seasons to compare the pollution characteristics with varying precipitation levels and flow rates. The highest level of urbanization is present in the upstream areas of the Hwangguji and Osan Streams. Most of the water quality parameter values were the highest in the downstream areas after the polluted rivers merged. The results showed a dilution effect with a lower pollution level in the wet season. Conversely, the daily unit area pollutant load was higher in the rainy season, indicating that the pollutants increased as the flow rate increased. A cluster analysis identified that the downstream water quality parameters are quite different from the upstream values. Upstream is an urban area with relatively high organic matter and nutrient loads. The upstream sewage treatment facilities were the main pollution sources. This study provides basic data for policymakers in urban water quality management.

Research on Soft Sensor Modeling Method and Its Application in Wastewater Treatment

In order to solve the problem of real-time monitoring of water quality index which is difficult to measure in the process of wastewater treatment, a dynamic modeling method of soft sensor based on the VIP (Variable Importance in Projection) and JIT (Just-in-Time) methods was proposed. Firstly, the important variables were selected by the VIP method, and then the data were updated by the JIT method. Finally, the soft sensing model of PLS (partial least squares) was established. The model was used to predict BOD5 which was an important water quality parameter in the process of wastewater treatment. The simulation results indicate that the proposed model achieves better mean square error and correlation coefficient.

Application of fuzzy evaluation technique and grey clustering method for water quality assessment of the coastal and estuaries of selected rivers in Sarawak

Background Estuarine and marine water quality has remarkable importance because these water resources are used for multiple reasons for instance: transportation, tourism, recreation, and other human or economic ways to use water. The objective of the study was to assess the water quality of the coastal and estuaries of the Rambungan, Sibu, Salak, and Santubong rivers in Sarawak, Malaysia. Water samples were collected from 10 locations and analyzed by employing standard techniques. A fuzzy comprehensive evaluation, grey clustering evaluation methods, Thailand Marine Water Classification System, and the Malaysian Marine Water Quality Index (MMWQI) and its classification system were applied to compute the index of each water quality parameter. Results The results showed that all the analyzed water quality parameters were within the allowable threshold levels. The results obtained by the application of fuzzy comprehensive evaluation and grey clustering evaluation methods proved that the coastal and the estuaries waters were clean with exception of coastal location CZ9 and the estuary of Salak river which showed slight pollution. Based on the Malaysian Marine Water Quality Index, it was observed that all the locations were in the classification group of moderate (i.e. 50–79%). This suggests that the estuaries of selected rivers can be used for natural resource conservation, while the coastal regions are good for fish farming. Conclusion It can be deduced that the suggested techniques were workable and logical. The method developed and the information in this study can serve as a reference and decision support for scientists and policymakers of concern.

Study of IoT Based Smart Water Quality Monitoring System

IoT based water quality parameter monitoring system is a significant interest in the field of cost-effective smart water quality monitoring systems. As we know that the population growth of your country is high in last few decades. In India, the demand for freshwater for drinking purposes, agriculture, and other activities is much higher than compared to other countries. The requirement of a smart water quality parameter monitoring system is necessary to reduce the time required in the traditional approach of water quality monitoring, and for real time monitoring. This literature survey work has been conducted in the field of smart water quality parameter monitoring systems. Sensor-based smart water quality parameter monitoring in past some research carried out which is deployed in the water.

Investigating use of chlorine dioxide for pre-treatment of raw water from polluted sources at Morton Jaffray Water Treatment Works, Harare, Zimbabwe

Raw water quality deterioration has affected capabilities of Conventional Water Treatment Methods (CWTM) in many countries. CWTM used at Morton Jaffray Water Treatment Works (MJWTW) in Harare have proven ineffective due to pollution. The study investigated the use of Chlorine Dioxide (ClO2) as an alternative pre-treatment chemical. Its effectiveness was compared to that of Calcium Hypochlorite (Ca(CIO)2) used at MJWTW. Grab raw water samples from MJWTW were collected between January and March 2020 and pre-treated with ClO2 and Ca(CIO)2 followed by jar tests with alum to determine pre-treatment effectiveness. Parameters analyzed included Electrical Conductivity (EC), Total Dissolved Solids (TDS), Total Solids (TS), turbidity, Chemical Oxygen Demand (COD), pH and total algae counts (TAC). The raw water had mean TDS (264 mg/L), TS (440 mg/L), turbidity (7.1 NTU), COD (85 mg/L), pH (7.9) and TAC (28.4 × 106 cells/mL). The optimum alum dosage without pre-oxidation was 80 mg/L. Pre-oxidation with 0.075 mg/L ClO2 reduced alum dosage to 60 mg/L. This ClO2 dosage was compared to a Ca(CIO)2 dosage of same concentration and strength at 5 and 15-minutes contact time before alum dosage at 60 mg/L. The treated water quality parameter levels proved better performance for ClO2 compared to calcium hypochlorite.

Monitoring Total Suspended Sediment Concentration in Spatiotemporal Domain over Teluk Lipat Utilizing Landsat 8 (OLI)

Total suspended sediment (TSS) is a water quality parameter that is used to understand sediment transport, aquatic ecosystem health, and engineering problems. The majority of TSS in water bodies is due to natural and human factors such as brought by river runoff, coastal erosion, dredging activities, and waves. It is an important parameter that should be monitored periodically, particularly over the dynamic coastal region. This study aims to monitor spatiotemporal TSS concentration over Teluk Lipat, Malaysia. To date, there are two commonly used methods to monitor TSS concentration over wide water regions. Firstly, field sampling is known very expensive and time-consuming method. Secondly, the remote sensing technology that can monitor spatiotemporal TSS concentration freely. Although remote sensing technology could overcome these problems, universal empirical or semiempirical algorithms are still not available. Most of the developed algorithms are on a regional basis. To measure TSS concentration over the different regions, a new regional algorithm needs to develop. To do so, two field trip was conducted in the study area concurrent with the passing of Landsat 8. A total of 30 field samples were collected from 30 sampling points during the first field trip and 30 samples from 30 samplings from the second field trip. The samples were then analyzed using an established method to develop the TSS algorithm. The data obtained from the first field trip were then used to develop a regional TSS algorithm using the regression analysis technique. The developed algorithm was then validated by using data obtained from the second field trip. The results demonstrated that TSS in the study area is highly correlated with three Landsat 8 bands, namely green, near-infrared (NIR), and short-wavelength (SWIR) bands, with R2 = 0.79. The TSS map is constructed using the algorithm. Analyses of the image suggest that the highest TSSs are mainly observed along the coastal line and over the river mouth. It suggested that the main contributing factors over the study area are river runoff and wave splash.