Main Quality Parameters Research Articles

Mitigating Salt Stress with Biochar: Effects on Yield and Quality of Dwarf Tomato Irrigated with Brackish Water.

The increase in the frequency and magnitude of environmental stresses poses a significant risk to the stability of food supplies. In coastal areas of the Mediterranean, brackish water has long been considered a limitation on horticultural production. In this scenario, the use of biochar in agriculture could be considered a valuable tool to cope with the deleterious effects of salt stress. This work aimed to investigate, in a protected environment, the effects of different concentrations of biochar (0, 1, and 2% v/v) obtained from poplar (Populus L.) biomass on the yield and quality of dwarf San Marzano ecotype tomatoes irrigated with saline water at different concentrations of NaCl (0, 40 and 80 mM). The increase in salt concentration from 0 to 80 mM NaCl reduced the total yield (-63%) and the number of fruits (-25%), but improved the main quality parameters such as dry matter (+75%), total soluble solids (+56%), and polyphenol content (+43%). Compared to control conditions, biochar supplementation improved the total yield (+23%) and number of fruits (+26%) without altering the functional and organoleptic characteristics of the fruits. The promising results underscore the potential of biochar as a sustainable solution to amend soils in order to improve tomato production under unfavorable conditions such as high salinity. However, there is a need to clarify which adaptation mechanisms triggered by biochar amending improve production responses even and especially under suboptimal growing conditions.

Machine learning for particle size prediction in iron ore grinding process

The mining industry is finally embracing the new opportunities brought by Industry 4.0, and one opportunity to be explored is the use of machine learning and artificial intelligence tools. To meet the continuous global demand for iron ore and improve the performance of facilities, process optimization is essential and may revolutionize the industry with data-based decision-making and increased efficiency and profitability. In iron ore beneficiation plants, the potential gains from machine learning tools are significant, and tend to increase in the milling process, where the grinding phase incurs the highest energy costs among mineral processing operations. This article presents the application of machine learning techniques for the prediction of the main product quality parameter of a milling plant. CRISP-DM, a well-established approach for data science projects, was applied, and the results demonstrated the effectiveness of the model, achieving satisfactory predictive performance. The deployment reduces reliance on laboratory testing and provides energy savings through proactive process control optimization.

High-altitude aquatic ecosystems offer faster aging rate of plastics

While research on the aging behavior of plastics in aquatic systems is extensive, studies focusing on high-altitude ecosystems, characterized by higher solar radiation and lower temperatures, remain limited. This study investigated the long-term aging behavior of non-biodegradable plastics (non-BPs), namely polyethylene terephthalate (PET) and polypropylene (PP) and biodegradable plastics (BPs), specifically polylactic acid plus polybutylene adipate-co-terephthalate (PLA + PBAT) and starch-based plastic (SBP), in a tributary of the Yarlung Zangbo River on the high-altitude Tibetan Plateau. Over 84 days of field aging, all four types of plastics exhibited initial rapid aging followed by deceleration. This aging process can be divided into two phases: rapid surface oxidation aging and an aging plateau phase. Notably, PP aged at a rate comparable to BPs, contrary to expectations of faster aging for BPs. Compared to low-altitude aquatic ecosystems, plastics in this study showed a faster aging rate. This was primarily due to intense ultraviolet radiation causing severe photoaging. Furthermore, the lower temperatures contributed to the formation of thinner biofilms. These thinner biofilms exhibited a reduced capacity to block light, further exacerbating the photoaging process of plastics. Statistical analysis results indicated that temperature, total nitrogen TN, and total phosphorus TP were likely the main water quality parameters influencing plastic aging. The varying effects of water properties and nutrients underscore the complex interaction of water quality parameters in high-altitude environments. Given the delicate nature of the high-altitude environment, the environmental impact of plastics, especially BPs, warrants careful consideration.

Drying kinetics of camellia oleifera seeds under hot air drying with ultrasonic pretreatment

Camellia oleifera seeds with high moisture content are prone to decay, leading to oil rancidity and seriously affecting subsequent processing and tea oil quality. Thus, an efficient drying method is needed. Hot air drying with ultrasonic pretreatment is a potential drying method for camellia oleifera seeds. In this paper, the effects of key parameters such as ultrasound power, ultrasound pretreatment time, and hot air drying temperature on the moisture ratio of camellia oleifera seeds, as well as the changes in microstructure of both seed shell and seed kernel, and the main quality parameters such as peroxide value and acid value of tea oil were explored. Results showed that the shorter the ultrasound pretreatment time, the greater the ultrasound power, the higher the temperature, the faster the drying rate, and the shorter the drying time. The drying time and specific energy consumption was reduced by up to 26.7 % and 16.8 % with the ultrasonic pretreatment, respectively. The optimal ultrasonic pretreatment time is 2 min, and the ultrasonic power is 300 W. A Genetic Algorithm Optimized Backpropagation Artificial Neural Network (GA-BP-ANN) drying model was proposed to predict the changes in moisture ratio during the drying process of camellia oleifera seeds to provide guidance for the development of intelligent drying system. The experimental results showed that the proposed drying model had a good predictive performance, with an RMSE of 0.0076969 and an R2 of 99.909 %, which can accurately estimate the effect of ultrasonic pretreatment on hot air drying. The research results of this study are expected to reduce post-harvest losses of camellia oleifera fruits and provide theoretical support for the development of drying systems of camellia oleifera seeds and similar agricultural products.

Harnessing the Role of Three Lactic Acid Bacteria (LAB) Strains for Type II Sourdough Production and Influence of Sourdoughs on Bread Quality and Maillard Reaction Products.

This study tested the effect of Companilactobacillus paralimentarius E-106, Lactiplantibacillus paraplantarum N-15 and Lactiplantibacillus plantarum SC-9 on the amount of Maillard reaction and aroma profile in bread making with main bread quality parameters. The specific volumes of sourdough and control breads were in the range of 2.97-3.04 cm3/g, and the control II bread had the highest hardness values on all days. The FAST index value was determined to be between 40.48% and 81.22% in all breads. The FAST index value was found to be higher in the control breads than in the sourdough breads. In the volatile compounds analysis, 72 volatile compounds were detected. The variety of volatile compounds in the breads with sourdough addition was higher than the control breads. Among the tested strains, Companilactobacillus paralimentarius E-106 demonstrated superior properties for bread characteristics in comparison to other strains as a type II sourdough starter. In summary, improved aroma profile and decreased Maillard reaction products can be provided by sourdough addition without changing the bread quality, along with meeting consumer demand for less additive use.

Postharvest Quality of Citrus medica L. (cv Liscia-Diamante) Fruit Stored at Different Temperatures: Volatile Profile and Antimicrobial Activity of Essential Oils.

Citron (Citrus medica L. cv. Liscia-diamante), cultivated in the "Riviera dei Cedri" (southern Italy), is mainly utilized in the production of candied fruit and essential oils (EOs). Up to now, no information regarding the effect of storage temperatures on citron has been reported. Here, citron samples, after harvesting, were stored at different temperatures (5, 10 and 20 °C at 70% relative humidity) for two weeks, and the main postharvest quality parameters were evaluated. Moreover, EOs extracted from the stored samples were chemically characterized to reveal changes in the volatiles profile and antimicrobial activity. The EOs presented monoterpene hydrocarbons (87.1 to 96.3% of the total oil profile) as the most abundant compounds, followed by oxygenated metabolites ranging from 9.7 to 3.1% of the total pattern. Postharvest quality traits showed a good retention of green peel color during storage at 5 °C, while EOs from samples stored for 7 and 14 days at 10 and 20 °C, respectively, showed the highest antimicrobial activity against most assayed strains. The results indicated storage at 10 °C for 7 days as the most suitable for the preservation of the postharvest quality of the fruit and the antimicrobial activity of the extracted EOs.

Challenges and Opportunities for Pilot Scaling-Up Extraction of Olive Oil Assisted by Pulsed Electric Fields: Process, Product, and Economic Evaluation

This study aimed to investigate the impact of Pulsed Electric Fields (PEF) technology in the extraction of olive oil on a pilot scale, using the “Galega Vulgar” olive variety as raw material. The extraction assisted by PEF had a malaxation time of 30 min and was compared with the traditional process of 45 min of malaxation. The main quality parameters of olive oil and the PEF’s cost-benefit assessment were performed. The incorporation of PEF in olive oil production reduced the malaxation stage by 33% without compromising the yield or extra-virgin classification. This efficiency leads to a potential 12.3% increase in annual olive oil production, with a 12.3% and 36.8% rise in revenue and gross profit, respectively. For small-scale production, the considerable upfront investment required for PEF equipment may be a challenge in terms of return on investment. In this scenario, opting for a renting scheme is the best economic solution, especially given the seasonal nature of olive oil production. In medium- to large-scale production, the investment in PEF is a sound investment since it is possible to achieve, with an equipment cost of EUR 450,000 and a production output of 5 tons per hour, an annual ROI of 20%.

Associations between bedroom environment and sleep quality when sleeping less or more than 6h: A cross sectional study during summer

A cross-sectional study was conducted in 50 bedrooms in Shanghai, each for one week during summer, to investigate the effects of the bedroom environment on sleep quality. Sleep quality was recorded with a wrist-worn sleep tracker and assessed with a questionnaire. The measurements from 168 person-nights were analysed after excluding unreliable data. The overnight means of bedroom temperature, relative humidity, CO2 and PM2.5 concentrations, which were continuously measured were between 21 to 30 °C, 43 to 86 %, 375 to 2756 ppm, and <1 to 43 μg/m3, respectively. Sleep duration in 36.3 % of the nights was less than 6 h, which is too short according to the recommendation of the American Sleep Foundation. The nights were then classified into two categories, with sleep duration above or below 6 h. The results show that the sleep quality in the short-sleep category was more sensitive to the bedroom environment. In the longer-sleep duration category, light sleep decreased with higher humidity and increased with higher PM2.5 concentration. In the short-sleep duration category, all the main sleep quality parameters including sleep efficiency and the duration and percentage of deep and rapid eye movement sleep stage decreased, and time spent awake increased with increased bedroom temperature, humidity, CO2 and PM2.5 concentration. The occupants who used air conditioning had a lower indoor temperature and humidity at night; opening windows decreased the indoor CO2 concentration but increased the PM2.5 concentration. These results suggest the need for alternative solutions to natural ventilation during summer in dwellings in Shanghai.

Near-Infrared Spectroscopy and Chemometrics for Effective Online Quality Monitoring and Process Control during Pelagic Fishmeal and Oil Processing.

Near-infrared spectroscopy has become a common quality assessment tool for fishmeal products during the last two decades. However, to date it has not been used for active online quality monitoring during fishmeal processing. Our aim was to investigate whether NIR spectroscopy, in combination with multivariate chemometrics, could actively predict the changes in the main chemical quality parameters of pelagic fishmeal and oil during processing, with an emphasis on lipid quality changes. Results indicated that partial least square regression (PLSR) models from the NIR data effectively predicted proximate composition changes during processing (with coefficients of determination of an independent test set at RCV2 = 0.9938, RMSECV = 2.41 for water; RCV2 = 0.9773, RMSECV = 3.94 for lipids; and RCV2 = 0.9356, RMSECV = 5.58 for FFDM) and were successful in distinguishing between fatty acids according to their level of saturation (SFA (RCV2=0.9928, RMSECV=0.24), MUFA (RCV2=0.8291, RMSECV=1.49), PUFA (RCV2=0.8588, RMSECV=2.11)). This technique also allowed the prediction of phospholipids (PL RCV2=0.8617, RMSECV=0.11, and DHA (RCV2=0.8785, RMSECV=0.89) and EPA content RCV2=0.8689, RMSECV=0.62) throughout processing. NIR spectroscopy in combination with chemometrics is, thus, a powerful quality assessment tool that can be applied for active online quality monitoring and processing control during fishmeal and oil processing.

Kinetics of Nonenzymatic Browning Reactions in Pumpkin Puree During Storage

Pumpkins (Cucurbita moschata) are a great source of essential nutrients counting vitamins, minerals, carotenoids, and dietary fiber. Due to their healthy composition, it draws consumer attention. Pumpkin puree is used for preparation of baby foods, soups, flour, jam, jellies, and desserts. To assess the main quality parameters of the foods such as color and hydroxymethylfurfural (HMF) formation, CIE-L*a*b* color changes and HMF formation which is also an indicator of Maillard browning reactions were evaluated storing the pumpkin puree at 27°, 37° and 47°C for 17 weeks. Kinetic parameters for HMF formation and color changes were calculated. The findings showed that HMF quantity linearly increased with the temperature and storage duration following the reaction model of zero order. The values of b* coordinate, Lightness, Chroma, and hue lessened linearly as a* coordinate values increased linearly fitting zero order reaction kinetic. By means of Arrhenius equation, the temperature dependency of the rate constant of color variation was demonstrated and the values of the activation energy (kj mol-1) were calculated as 76.15, 30.60, 46.08, 28.44, 27.61 and 38.32 for HMF formation, Lightness, a* coordinate, b* coordinate, Chroma, and hue, respectively.

Water quality assessment and evaluation of human health risks in the Pusur River, Klaten Regency, Central Java, Indonesia.

This research examines the condition of the water characteristics of the Pusur River to provide data regarding water quality status and risks to human health. In determining the water quality use the NSF-WQI, or National Sanitation Foundation-Water Quality Index based on the main water quality parameters, including temperature, fecal coliform, turbidity, pH, DO, COD, and nitrate. The WQI at the Sudimoro Village location is 37.50, Cokro is 43.54, Wangen is 39.51, Delanggu is 35.45, Juwiring is 38.05, and Taji Village is 34.92. From these six locations, the WQI values are all included in the bad criteria (scores of 26-50). The low WQI values at the six locations were influenced by the low values of the DO, nitrate, temperature, and fecal coliform sub-indices. This low value is related to anthropogenic activities in the region. Furthermore, the data were subjected to applications of the Chronic Daily Index (CDI) Hazard Quotient (HQ), and Hazard Index (HI) to reveal possible non-carcinogenic health risks from nitrate and cadmium contaminants. The hazard quotient through exposure to ingestion from each sampling point includes low risk, namely the value range of 0.1 < HI < 1 for adults and children. Water from rivers must be adequately treated before being used for domestic purposes.

Estimating the water quality index based on interpretable machine learning models.

The water quality index (WQI) is an important tool for evaluating the water quality status of lakes. In this study, we used the WQI to evaluate the spatial water quality characteristics of Dianchi Lake. However, the WQI calculation is time-consuming, and machine learning models exhibit significant advantages in terms of timeliness and nonlinear data fitting. We used a machine learning model with optimized parameters to predict the WQI, and the light gradient boosting machine achieved good predictive performance. The machine learning model trained based on the entire Dianchi Lake water quality data achieved coefficient of determination (R2), mean square error, and mean absolute error values of 0.989, 0.228, and 0.298, respectively. In addition, we used the Shapley additive explanations (SHAP) method to interpret and analyse the machine learning model and identified the main water quality parameter that affects the WQI of Dianchi Lake as NH4+-N. Within the entire range of Dianchi Lake, the SHAP values of NH4+-N varied from -9 to 3. Thus, in future water environmental governance, it is necessary to focus on NH4+-N changes. These results can provide a reference for the treatment of lake water environments.

Impact of Limestone Surface Impurities on Quicklime Product Quality

Quicklime is produced through the thermal processing of limestone in industrial kilns. During quarry operations, fine particulate quarry dust adheres to limestone lump surfaces, increasing the bulk concentration of impurities in limestone products. During thermal processing in a kiln, impurities such as Si, Mg, Al, Fe, and Mn react with Ca, reducing quicklime product quality. Which reactant phases are formed, and the extent to which these result in a reduction in quality, has not been extensively investigated. The present study investigated as-received and manually washed limestone product samples from two operational quarries using elemental compositions and a developed predictive multi-component chemical equilibrium model to obtain global phase diagrams for 1000–1500 °C, corresponding to the high-temperature zone of a lime kiln, identifying phases expected to be formed in quicklime during thermal processing. The results suggest that impurities found on the surface of the lime kiln limestone feed reduce the main quality parameter of the quicklime products, i.e., calcium oxide, CaO (s), content by 0.8–1.5 wt.% for the investigated materials. The results also show that, in addition to the effect of impurities, the quantity of CaO (s) varies greatly with temperature. More impurities result in more variation and a greater need for accurate temperature control of the kiln, where keeping the temperature below approximately 1300 °C, that of Hatrurite formation, is necessary for a product with higher CaO (s).

An Integrated Architecture for Robotic Assembly and Inspection of a Composite Fuselage Panel with an Industry 5.0 Perspective

Aeronautical robotic applications use quite large, heavy robots with huge end effectors that are frequently multifunctional. An assembly jig to hold a fuselage panel and two medium-sized six-axis robots fixed on linear axes, referred to as the internal and the external robot with respect to the curvature of the panel, make up the Lean robotized AssemBly and cOntrol of composite aeRostructures (LABOR) work cell. A distributed software architecture is proposed in which individual modules are developed to execute specific subprocesses, each implementing innovative algorithms that solve the main drawbacks of state-of-the-art solutions. Real-time referencing adopts a point-cloud-based strategy to reconstruct and process the part before drilling, avoiding hole positioning errors. Accurate concentric countersink diameters are made possible through the automatic adjustment of the drilling tool with respect to the skin panel, which guarantees its orthogonality, as well as the implementation of process parameter optimization algorithms based on historical results that compensate for the wear of the drilling bits. Automatic sealing and fastening strategies that involve the measurement of the main fastener quality parameters allow for the complete verification of the entire assembly process of each part. Additionally, an advanced multimodal perception system continuously monitors the collaborative workspace to ensure safe human–robot collaboration (HRC) tasks. Through this integrated architecture, LABOR substantially reduces expenses and facilitates maintenance and programming.

Application of digital image processing method for roasted coffee bean quality identification: a systematic literature review

In coffee processing, there are several important stages, one of which is roasting. The roasting process is an important determinant of coffee quality. Determination of coffee quality can be done using digital image processing methods to produce parameters and quality classifications precisely, make images of better quality so that photos and moving images can be easily understood. This analysis uses a Systematic Literature Review (SLR) for the identification, evaluation, and interpretation of all available research results on the topics discussed. The purpose of this study was to identify and analyze the main quality parameters and the best digital image processing methods used in classifying the quality of roasted coffee beans. From the results of the analysis of 31 journals, it is known that the parameters for evaluating the quality of roasted coffee are color parameters, texture parameters, and shape parameters. The color parameters consist of Red Green Blue (RGB), Grayscale, Hue Saturation Intensity (HSI), and L*a*b* features. The texture parameters consist of energy, entropy, homogeneity, and contrast. As for the feature shape parameters, they are area, circumference, diameter, and percentage of roundness. Results of the analysis show that the main parameter that plays an important role in assessing the quality of roasting coffee is the color parameter. This can be seen from the function of the color parameter in quality identification based on the image of the roasted coffee beans. The quality parameters used are image capture, image resolution, training data, testing data, iterations, and accuracy values. In addition, the resulting image processing methods used for quality classification include Backpropagation (BP), Learning Vector Quantization (LVQ), and K-Nearest Neighbor (KNN). Based on results of the analysis, the best method for classifying the quality of roasting results is Backpropagation, and it is known that the accuracy value of this method has a high range of values. Key words: Backpropagation, K-Nearest Neighbour, Learning Vector Quantization, Coffee Bean Roasting, Image Processing

ARIMA and TFARIMA Analysis of the Main Water Quality Parameters in the Initial Components of a Megacity’s Drinking Water Supply System

The objective of this paper is to use autoregressive, integrated, and moving average (ARIMA) and transfer function ARIMA (TFARIMA) models to analyze the behavior of the main water quality parameters in the initial components of a drinking water supply system (DWSS) of a megacity (Bogota, Colombia). The DWSS considered in this study consisted of the following components: a river, a reservoir, and a drinking water treatment plant (WTP). Water quality information was collected daily and over a period of 8 years. A comparative analysis was made between the components of the DWSS based on the structure of the ARIMA and TFARIMA models developed. The results show that the best water quality indicators are the following: turbidity &gt; color &gt; total iron. Increasing the time window of the ARIMA analysis (daily/weekly/monthly) suggests an increase in the magnitude of the AR term for each DWSS component (WTP &gt; river &gt; reservoir). This trend suggests that the turbidity behavior in the WTP is more influenced by past observations compared to the turbidity behavior in the river and reservoir, respectively. Smoothing of the data series (moving average) as the time window of the ARIMA analysis increases leads to a greater sensitivity of the model for outlier detection. TFARIMA models suggest that there is no significant influence of past river turbidity events on turbidity in the reservoir, and of reservoir turbidity on turbidity at the WTP outlet. Turbidity outlier events between the river and reservoir occur mainly in a single observation (additive outliers), and between the reservoir and WTP also have a permanent effect over time (level shift outliers). The AR term of the models is useful for studying the transfer of effects between DWSS components, and the MA term is useful for studying the influence of external factors on water quality in each DWSS component.

Utilization of Blackthorn Plums (Prunus spinosa) and Sweet Cherry (Prunus avium) Kernel Oil: Assessment of Chemical Composition, Antioxidant Activity, and Oxidative Stability

Prunus avium L. and Prunus spinosa L. are valuable fruit-bearing trees known for their bioactive compounds and medicinal properties. However, limited research exists regarding their kernel oils. This study aimed to compare the chemical composition, quality parameters, and bioactive potential of the kernel oils extracted from Prunus avium L. and Prunus spinosa L. The kernel oils’ fatty acid and tocopherol profiles were characterized, and the presence of bioactive compounds were identified and quantified. Total polyphenol content (TPC) and antioxidant activity (AAC) were also measured, indicating the presence of bioactive compounds in both oils. Additionally, the main quality parameters, including oxidative status, were evaluated. The fatty acid analysis revealed a higher proportion of polyunsaturated fatty acids compared to monounsaturated fatty acids in both kernel oil samples. Linoleic acid (57–64%) and oleic acid (18–29%) were the major fatty acids in both Prunus avium L. and Prunus spinosa L. kernel oils. α-Eleostearic acid (11.87%) was quantified only in Prunus avium kernel oil. Furthermore, the α-, β-, γ-, and δ-tocopherol content were determined, and it was found that both kernel oils contained γ-tocopherol as the major tocopherol (~204–237 mg/Kg). TPC in Prunus avium L. kernel oil was measured at 9.5 mg gallic acid equivalents (GAE)/Kg and recorded as ~316% higher TPC than Prunus spinosa L. kernel oil. However, the recorded AAC were 11.87 and 14.22 μmol Trolox equivalent (TE)/Kg oil, respectively. Both oils recorded low peroxide values (~1.50 mmol H2O2/Kg), and low TBARS value (~0.4 mmol malondialdehyde equivalents, MDAE/Kg oil), but high p-anisidine value (23–32). The results indicated that both Prunus avium L. and Prunus spinosa L. kernel oils exhibited unique chemical compositions.

Conjugation of deep learning and de noising data methods for short-term water turbidity forecasting

Water turbidity is a critical index of water quality due to its high correlation with the five main water quality parameters (electrical conductivity, nitrogen, dissolved oxygen, phosphorus, and pH). The exact measurement of water turbidity is a difficult process because many conditions affect the reading of turbidity. Although many researchers applied decomposition-based techniques for preprocessing, it is difficult to use these approaches in real estimation because the newly acquired data greatly affect the initial decomposed subsequent values. In this study, the threshold-based wavelet denoising method, as a data pre-processing, coupled with the deep learning models (i.e., ANN and ANFIS) was employed to enhance the performance of the water turbidity modeling. The results showed that deep learning techniques in temporal modeling of water turbidity have good accuracy and can be used with reasonable confidence. Also, data denoising increases the accuracy of deep learning methods in estimating the amount of water turbidity. ANFIS method is more accurate in both calibration and validation modes as well as in noisy and denoised conditions. Based on the results, data denoising in the ANN method has a more significant impact than in the ANFIS technique. For example, in Comb. 5, which is the best case, the improvement rate of the results in the ANN is 12% and in the ANFIS method is 4%. This could be due to the fuzzy system in handling uncertainties in the model.

Effects of source-sink landscape proximity on the spatial–temporal water quality from the perspective of cost distance in an agricultural watershed

Spatial patterns of source-sink landscapes play a critical role in assessing surface water quality. However, this relation may be different due to various proximity of source-sink landscapes. The effect of the source-sink landscape proximity on surface water quality remains poorly understood. The impact of source-sink landscape proximity on the spatial–temporal water quality were quantified based on field monitoring data (2013–2019) in an agricultural watershed. A new approach considering the cost distance of landscape types, slope, and vegetation on the transformation of nutrients from one point to water bodies was used to construct different buffer zones which reflected very high, high, moderate, low, very low proximities, while the subwatershed represented the very low proximity. Then the effects of the source-sink landscape proximity were assessed based on sub-watershed and other four buffers. The resutls showed that the TN and NO3−-N were the main non-point source pollution indices with significant spatial variation (p < 0.05). The water quality index (WQI) was higher during the dry season than during the wet season, indicating that the sampling sites also experienced seasonal variations. The multi-scale analysis demonstrated the significance of the source-sink landscape proximity in improving the quality of surface water. A stronger correlation between the ratios of sink landscape and the main water quality parameters (the TN and NO3−-N) was observed in the low proximity zone, suggestiong that the low proximity zone with moderate non-point source pollution risk was a critical region in water quality management. The results provide important insights into understanding the effects of the source-sink landscape proximity on water quality and how scientific and effective measures can be made to improve water quality.

Turbidity Reduction by Using Variations of Filtration Media Sizes (Case Study of Treated Water PT. X Jakarta)

Turbidity is one of the main water quality parameters in drinking water quality. One way to reduce turbidity levels is by filtration. The filtration unit in the water treatment plant must use appropriate media so that the processed raw water meets World Health Organization (WHO) standards. WHO states that the turbidity of processed water before disinfection is 1 NTU. This research was conducted to know the efficiency of filtration processing with various media in reducing the concentration of turbidity in raw water. This study used three types of media, namely 50% silica sand with a mesh size of 12-16 and 50% silica sand with a mesh size of 8-12 (C), 100% silica sand with a mesh size of 12-16 (K), and 100% silica sand in mesh size. 8-12 (B). Porosity value for each sample C, K, and B is 0.412; 0.423; and 0.388. The detention time at maximum discharge for samples C, K, and B was 3.6, respectively; 3.76; and 3.45 minutes. The flow rates used for media samples C, K, and B were 4125 L/s and 2500 L/s, respectively. The removal efficiency produced by the highest turbidity was obtained in medium C with values of 90.8% and 89.9%, respectively. Meanwhile, media B gave the lowest turbidity removal efficiency, namely 86.3% and 86.4%. The results of all variations of the media used have met WHO standards, where the processed results have produced turbidity concentrations of 0.25 - 0.32 NTU.