Modeling of the residue transport of lambda cyhalothrin, cypermethrin, malathion and endosulfan in three different environmental compartments in the Philippines

AbstractThis study aims to determine the environmental transport and fate of the residue of four Philippines priority chemicals; i.e., lambda cyhalothrin (L-cyhalothrin), cypermethrin, endosulfan and malathion, in three different environmental compartments (air, water and soil). In the Philippines, pesticide application is the most common method of controlling pests and weeds in rice and vegetable farming. This practice aided the agricultural industry to minimize losses and increase yield. However, indiscriminate use of pesticides resulted to adverse effects to public health and environment. Studies showed that 95% of the applied pesticides went to non-target species. Data from previous studies in Pagsanjan Laguna, Philippines were used as input data. Dispersion, Gaussian plume, and regression equations were employed to simulate the behavior of L-cyhalothrin, cypermethrin, endosulfan and malathion in air, water and soil. Substance decay was calculated using first order reaction. This study showed how L-cyhalothrin, cypermethrin, endosulfan, and malathion behaved in the environment after release from nozzle spray, and its possible duration of stay in the environment. It will also show a tool in determining the percolation depth through soil by endosulfan. This tool can be utilized in determining the depth of contaminated soil during remediation strategic planning and project implementation of similar environmental condition

Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method

This article discusses the assessment of groundwater quality using a hybrid technique that would aid in the convenience of groundwater (GW) quality monitoring. Twenty eight (28) GW samples representing 62 barangays in Calapan City, Oriental Mindoro, Philippines were analyzed for their physicochemical characteristics and heavy metal (HM) concentrations. The 28 GW samples were collected at suburban sites identified by the coordinates produced by Global Positioning System Montana 680. The analysis of heavy metal concentrations was conducted onsite using portable handheld X-Ray Fluorescence (pXRF) Spectrometry. Hybrid machine learning—geostatistical interpolation (MLGI) method, specific to neural network particle swarm optimization with Empirical Bayesian Kriging (NN-PSO+EBK), was employed for data integration, GW quality spatial assessment and monitoring. Spatial map of metals concentration was produced using the NN-PSO-EBK. Another, spot map was created for observed metals concentration and was compared to the spatial maps. Results showed that the created maps recorded significant results based on its MSEs with values such as 1.404 × 10−4, 5.42 × 10−5, 6.26 × 10−4, 3.7 × 10−6, 4.141 × 10−4 for Ba, Cu, Fe, Mn, Zn, respectively. Also, cross-validation of the observed and predicted values resulted to R values range within 0.934–0.994 which means almost accurate. Based on these results, it can be stated that the technique is efficient for groundwater quality monitoring. Utilization of this technique could be useful in regular and efficient GW quality monitoring

Modeling of CHB masonry for seismic analysis of low-rise reinforced concrete frames: Effects of CHB masonry properties

This paper presents a practical method of modeling CHB masonry for seismic analysis. CHB walls are commonly used in low-rise buildings as non-structural elements that are usually neglected in the analysis of frames as they are assumed not to carry any lateral forces during seismic activity. The inherent in-plane strength and stiffness of these walls attract substantial amount of in-plane forces which were solely assigned to carry by the compositing frame. These actions deviates the behavior of the frame by decreasing the natural period of the structure and correspondingly increase the applied seismic forces. Hence, the frame behavior cannot just be idealized as a simple bare frame but instead a CHB in-filled frame. It was therefore the objective of the study to model the CHB walls in order to determine its effects on the low-rise reinforced concrete frame. The method used in carrying out this study was the compression strut theory that represents the CHB walls as equivalent pin-jointed compression strut. The model considered the mechanical properties of local CHB masonry. Experimental test was conducted by the use of prism having four types of masonry mortars, two types of CHB units with two variable thicknesses. The resulting data were then used to calculate the equivalent thickness of compression struts which were subsequently modeled as cross-braced-type system for the frame. The assessment of the modeled frames was conducted through static pushover analysis using SAP 2000 in which produced capacity curves. Comparison of the capacity curves followed, i.e., for sixteen frame models with different CHB infill properties with bare frame model. Results showed that modeling CHB walls as infill compression struts in seismic analysis can significantly modify the behavior of the frame by increasing its strength and stiffness compared with the conventional bare frame approach. It was concluded that the effect of compression infill strut to the behavior of the frame had been significantly affected by the properties of CHB masonry. Hence, quality of CHB shall be given attention and shall be considered in the design process of concrete frame. That, failure to consider CHB properties to the frame analysis would potentially lead to property damage and adverse impact to public health

Flood Risk Assessment Using GIS-Based Analytical Hierarchy Process in the Municipality of Odiongan, Romblon, Philippines

The archipelagic Romblon province frequently experiences typhoons and heavy rains that causes extreme flooding, this produces particular concern about the severity of damage in the Municipality of Odiongan. Hence, this study aimed to assess the spatial flood risk of Odiongan using the analytical hierarchy process (AHP), considering disaster risk factors with data collected from various government agencies. The study employed the geographic information system (GIS) to illustrate the spatial distribution of flooding in the municipality. Sendai Framework was the basis of risk analysis in this study. The hazard parameters considered were average annual rainfall, elevation, slope, soil type, and flood depth. Population density, land use, and household number were considered parameters for the exposure assessment. Vulnerability assessments considered gender ratio, mean age, average income, number of persons with disabilities, educational attainment, water usage, emergency preparedness, type of structures, and distance to evacuation area as physical, social, and economic factors. Each parameter was compared to one another by pairwise comparison to identify the weights based on experts’ judgment. These weights were then integrated into the flood risk assessment computation. The results led to a flood risk map which recorded nine barangays (small local government units) at high risk of flooding, notably the Poblacion Area. The results of this study will guide local government units in developing prompt flood management programs, appropriate mitigation measures, preparedness, and response and recovery strategies to reduce flood risk and vulnerability to the population of Odiongan

Pollution and Risk Evaluation of Toxic Metals and Metalloid in Water Resources of San Jose, Occidental Mindoro, Philippines

Clean and safe drinking water is an integral part of daily living and is considered as a basic human need. Hence, this study investigated the suitability of the domestic water (DW) and groundwater (GW) samples with respect to the presence of metals and metalloid (MMs) in San Jose, Occidental Mindoro, Philippines. The MMs analyzed in the area of study for DW and GW were Arsenic (As), Barium (Ba), Copper (Cu), Chromium (Cr), Iron (Fe), Lead (Pb), Manganese (Mn), Nickel (Ni), and Zinc (Zn). The results revealed that Pb has the mean highest concentration for DW, while Fe is in GW resources in the area. Quality evaluation of DW and GW was performed using Metal Pollution Index (MPI), Nemerow’s Pollution Index (NPI), and Ecological Risk Index (ERI). The mean NPI value calculated for DW was 135 times greater than the upper limit of the unpolluted location category. The highest NPI observed was 1080 times higher than the upper limit of the unpolluted site category. That of the ERI observed in the area was 23.8 times higher than the upper limit for a “low” ERI category. Furthermore, the health risk assessment (HRA) of the GW and DW of the study area revealed non-carcinogenic health risks of the MMs analyzed in GW samples, and potential carcinogenic health risks from As, Cr, Pb, and Ni in DW. The use of machine learning geostatistical interpolation (MLGI) mapping to illustrate the PI and health risk (HR) in the area was an efficient and dependable evaluation tool for assessing and identifying probable MMs pollution hotspots. The data, tools, and the process could be utilized in carrying out water assessment, the evaluation leading to a comprehensive water management program in the area and neighboring regions of similar conditions

Development of Vulnerability Assessment Framework for Disaster Risk Reduction at Three Levels of Geopolitical Units in the Philippines

This study developed a comprehensive framework for vulnerability assessment as a tool to measure vulnerability at three levels of geopolitical units in the Philippines. This is a comprehensive multi-disaster framework that can provide information to a decentralized type of government system like the Philippines. The vulnerability assessment framework (VAF) that has been developed was anchored upon the IPCC model and used the integration of community-based monitoring system (CBMS) data, expert inputs, and a series of community-based activities such as consultative fora, focus group discussions, workshops, and risk reduction immersion activities. The developed VAF for the assessment of vulnerability indices (VIs) is a system framework composed of a vulnerability scoping diagram (VSD) and an expanded vulnerability assessment model (VAM). The VSD is composed of three dimensions (e.g., exposure, sensitivity, resiliency), seven identified hazards, with 26, 27, and 29 sub-indicators for household, barangay, and municipal levels, respectively. Measuring vulnerability can be an effective strategy for assessing the potential impact/s of natural disasters on society. The continuous occurrence of natural disasters in the Philippines requires enhancement of public understanding of vulnerability. This would provide transparent understanding and enhance community competency leading to the development of methodologies and tools to assess various factors and indicators of vulnerability. The information extracted from using the VAF and VSD are helpful to the local government units, especially in preparing budgets, strategies, and programs for disaster risk reduction

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality

Effects of pH and concentration on the capability of E. coli and S. epidermidis with bentonite clay as biosorbent for the removal of Copper, Nickel and Lead from polluted water

This paper discusses the effects of pH and concentration on the capability of E. coli ATCC29522 and S. epidermidis RP62A biofilm with bentonite in removing divalent copper, nickel and lead from wastewater. Batch adsorption study at laboratory scale was utilized to evaluate the potential use of bacterial biomass (E. coli ATCC29522 and S. epidermidis RP62A) aided with geosynthetic clay (bentonite) for the removal of Cu2+, Ni2+and Pb2+. Results revealed that removal of Cu2+, Ni2+and Pb2+ by both types of organisms supported with bentonite were high in the first 4 hours of the experiment. This illustrates that the binding site on that particular time was abundant. Hence, the removal rate was evident at high concentration depicting the line adsorption equilibrium. It also revealed that S. epidermidis RP62A supported with bentonite had the highest affinity to Copper and Lead with Qm = 277.7 mg/g and 5.0075 mg/g, respectively. While E. coli ATCC 29522 had the highest affinity to Nickel (Qm= 58.82 mg/g). Hence, the sorption of Cu2+, Ni2+and Pb2+ onto E. coli ATCC29522 and S. epidermidis RP62A biofilm supported with bentonite clay occurred through monolayer chemisorption on the homogeneous surface of E. coli ATCC29522 and S. epidermidis RP62A biofilm with bentonite clay. Batch kinetics studies revealed that the sorption of Cu2+, Ni2+and Pb2+ onto E. coli ATCC29522 and S. epidermidis RP62A biofilm supported with bentonite clay was well described by a pseudo-second-order equation model of type 1 (R2 = 0.9999), which implies that chemisorption is the rate limiting step