Pollution Control Department Research Articles

Effect of PM2.5 on burden of mortality from non-communicable diseases in northern Thailand.

Particulate pollution, especially PM2.5from biomass burning, affects public and human health in northern Thailand during the dry season. Therefore, PM2.5exposure increases non-communicable disease incidence and mortality. This study examined the relationship between PM2.5and NCD mortality, including heart disease, hypertension, chronic lung disease, stroke, and diabetes, in northern Thailand during 2017-2021. The analysis utilized accurate PM2.5data from the MERRA2 reanalysis, along with ground-based PM2.5measurements from the Pollution Control Department and mortality data from the Division of Non-Communicable Disease, Thailand. The cross-correlation and spearman coefficient were utilized for the time-lag, and direction of the relationship between PM2.5and mortality from NCDs, respectively. The Hazard Quotient (HQ) was used to quantify the health risk of PM2.5to people in northern Thailand. High PM2.5 risk was observed in March, with peak PM2.5concentration reaching 100 µg/m3, with maximum HQ values of 1.78±0.13 to 4.25±0.35 and 1.45±0.11 to 3.46±0.29 for males and females, respectively. Hypertension significantly correlated with PM2.5levels, followed by chronic lung disease and diabetes. The cross-correlation analysis showed a strong relationship between hypertansion mortality and PM2.5at a two-year time lag in Chiang Mai (0.73) (CI [-0.43-0.98], p-value of 0.0270) and a modest relationship with chronic lung disease at Lampang (0.33) (a four-year time lag). The results from spearman correlation analysis showed that PM2.5concentrations were associated with diabetes mortality in Chiang Mai, with a coefficient of 0.9 (CI [0.09-0.99], p-value of 0.03704). Lampang and Phayao had significant associations between PM2.5 and heart disease, with coefficients of 0.97 (CI [0.66-0.99], p-value of 0.0048) and 0.90 (CI [0.09-0.99], p-value of 0.0374), respectively, whereas Phrae had a high coefficient of 0.99 on stroke.

Assessment of Transboundary PM2.5 from Biomass Burning in Northern Thailand Using the WRF-Chem Model.

Air pollution, particularly PM2.5, poses a significant environmental and public health concern, particularly in northern Thailand, where elevated PM2.5 levels are prevalent during the dry season (January-May). This study examines the influx and patterns of transboundary biomass burning PM2.5 (TB PM2.5) in this region during the 2019 dry season using the WRF-Chem model. The model's reliability was confirmed through substantial correlations between model outputs and observations from the Pollution Control Department (PCD) of Thailand at 10 monitoring stations. The findings indicate that TB PM2.5 significantly influences local PM2.5 levels, often surpassing contributions from local sources. The influx of TB PM2.5 began in January from southern directions, intensifying and shifting northward, peaking in March with the highest TB PM2.5 proportions. Elevated levels persisted through April and declined in May. Border provinces consistently exhibited higher TB PM2.5 concentrations, with Chiang Rai province showing the highest average proportion, reaching up to 45%. On days when PM2.5 levels were classified as 'Unhealthy for Sensitive Groups' or 'Unhealthy', TB PM2.5 contributed at least 50% to the total PM2.5 at all stations. Notably, stations in Chiang Rai and Nan showed detectable TB PM2.5 even at 'Very Unhealthy' levels, underscoring the significant impact of TB PM2.5 in the northern border areas. Effective mitigation of PM2.5-related health risks requires addressing PM2.5 sources both within and beyond Thailand's borders.

Decadal Trends in Ambient Air Pollutants and Their Association with COPD and Lung Cancer in Upper Northern Thailand: 2013-2022.

Air pollution in upper northern Thailand raises health concerns. This study examined trends and associations between air pollutants and respiratory diseases, focusing on COPD and lung cancer during haze (December-May) and non-haze (June-November) seasons in upper northern Thailand from 2013 to 2022. This study utilized data from the Pollution Control Department and Chiang Mai Provincial Public Health. The key air pollutants included PM10, PM2.5, SO2, NO2, CO, and O3. Respiratory disease data included fatality rates for lung cancer and COPD and the re-admission rate for COPD. Results indicated peak air pollutant levels and COPD re-admission rates in March, with PM2.5 concentrations exceeding air quality standards from January to April. During haze periods, COPD fatality and re-admission rates significantly increased (mean difference: 0.43 and 4.23 per 1000-case population, respectively; p < 0.001), while lung cancer fatality rates were higher without statistical significance. Pearson correlation analysis found positive correlations between PM10, PM2.5, O3, and NO2 concentrations and COPD re-admission and fatality rates at 0-1 month lag times, with a declining trend observed at subsequent lag intervals of 2 to 3 months. Overall, this study highlights the predictable pattern of air pollution in the region, correlating with higher COPD fatality and re-admission rates.

A Situation and Temporal Behaviors of Air Pollution by ARIMA Model: A Case Study of Chiang Mai

The persistent issue of haze has become a paramount concern in various regions, impacting air quality, human health, and overall environmental well-being. This study focuses on the case of Chiang Mai, a city prone to recurrent haze times. This research reviews haze situation and its temporal behaviors to propose successive opportunities for effectively addressing the haze problem in Chiang Mai, encompassing both short-term mitigation measures and long-term sustainable strategies by applying haze data from 1996 to 2022 derived from Yupparaj school ground-based station recorded by Pollution Control Department (PCD) for analyzing the ARIMA statistics. According to the study's findings, the daily averages of PM10 and PM2.5 move greatly from January to April. There is a minor change in the daily average carbon monoxide (CO) between January and April. Although short-term projections in light of the area's pollution levels, it may be indicated that PM10, PM2.5, SO2, NO2, and O3 will likely increase, regarding the local pollution's changing behavior. When compared to other working days and weekends, it was discovered that the average CO values were significantly different at the 0.01 level, with Friday having the highest average CO emissions. Consideringly the timing, it was discovered that the CO2 levels were at their highest during rush hour, peaking in the evening and then the morning and noon periods, respectively. These characteristics indicate that measures taken to address this problem should concentrate on operating at a time that is relevant to the time the air pollution arises as well as the influences reducing and mitigating the level of air pollution greatest efficiency.

The Spatial–Temporal Emission of Air Pollutants from Biomass Burning during Haze Episodes in Northern Thailand

Air pollutants from biomass burning, including forest fires and agricultural trash burning, have contributed significantly to the pollution of the Asian atmosphere. Burned area estimates are variable, making it difficult to measure these emissions. Improving emission quantification of these critical air pollution sources requires refining methods and collecting thorough data. This study estimates air pollutants from biomass burning, including PMs, NOX, SO2, BC, and OC. Machine learning (ML) with the Random Forest (RF) method was used to assess burned areas in Google Earth Engine. Forest emissions were highest in the upper north and peaked in March and April 2019. Air pollutants from agricultural waste residue were found in the lower north, but harvesting seasons made timing less reliable. Biomass burning was compared to the MODIS aerosol optical depth (AOD) and Sentinel-5P air pollutants, with all comparisons made by the Pollution Control Department (PCD) Thailand air monitoring stations. Agro-industries, mainly sugar factories, produce air pollutants by burning bagasse as biomass fuel. Meanwhile, the emission inventory of agricultural operations in northern Thailand, including that of agro-industry and forest fires, was found to have a good relationship with the monthly average levels of ambient air pollutants. Overall, the information uncovered in this study is vital for air quality control and mitigation in northern Thailand and elsewhere.

Exploring the adverse effect of fine particulate matter (PM2.5) on wildland firefighters’ pulmonary function and DNA damage

Chiang Mai encounters severe pollution during the wildfire season. Wildland firefighters encounter various hazards while engaged in fire suppression operations, which encompass significant exposure to elevated concentrations of air pollutants resulting from combustion, especially particulate matter. The adverse effects of wildfire smoke on respiratory health are a significant concern. The objective of this study was to examine the potential adverse effects of PM2.5 exposure on the respiratory function and DNA damage of wildland firefighters. This prospective cohort study conducted in Chiang Mai from January to May 2022 planned to evaluate the health status of wildland firefighters during the pre-peak, peak, and post-peak ambient air pollution seasons. The measurement of PM2.5 was done at every forest fire station, as well as utilizing data from the Pollution Control Department. Participants received general health examinations, spirometry evaluations, and blood tests for DNA damage analysis. Pair t-tests and multiple regression models were used to examine the connection between pulmonary function parameters (FVC, FEV1) and PM2.5 concentration, with a significance level of P < 0.05. Thirty-three peak-season and twenty-one post-peak-season participants were enrolled. Four pre-peak-season wildland firefighters had FVC and FEV1 declines of > 15%. Multiple regression analysis showed a negative association between PM2.5 exposure and FVC% predicted (− 2.81%, 95% CI − 5.27 to − 0.34%, P = 0.027) and a marginally significant negative correlation with FVC (− 114.38 ml, 95% CI − 230.36 to 1.59 ml, P = 0.053). The remaining pulmonary measures showed a statistically insignificant decline. There were no significant changes in DNA damage detected. Wildland firefighters suffered a significant decline in pulmonary function associated with PM2.5 exposure. Spirometry is crucial for monitoring and promptly identifying respiratory issues that occur during wildfire seasons. Further research is recommended to explore DNA damage alterations and their potential association with PM2.5.

Using a Low-Cost Sensor to Estimate Fine Particulate Matter: A Case Study in Samutprakarn, Thailand

This study evaluates low-cost sensors (LCSs) for measuring coarse and fine particulate matter (PM) to clarify and measure air pollution. LCSs monitored PM10, PM2.5 (fine particulates), and PM1.0 concentrations at four sites in Samutprakarn, Thailand from December 2021 to April 2022. Average daily PM10, PM2.5, and PM1.0 concentrations at the monitoring locations were 53–79, 34–45, and 31–43 μg/m3, respectively. In December 2021, the monitoring station had a daily PM2.5 value above 100 μg/m3, indicating haze occurrences. However, the monitoring site’s daily PM10 and PM1.0 concentrations did not surpass Thailand’s ambient air quality threshold. We also measured and calibrated comparative particulate matter concentrations from LCSs and a tapered element oscillating microbalance (TEOM) monitor (Pollution Control Department (PCD) standard analytical method). PM2.5 concentrations from the LCSs were lower than TEOM, but the difference was not statistically significant. The PM2.5 monitoring station provided near-real-time air quality data for health risk reduction, especially when PM levels were high. Based on this study, authorities and local agencies may consider improving air quality regulation in Samutprakan, focusing on suburban PM2.5 air pollution.

Estimation of hourly one square kilometer fine particulate matter concentration over Thailand using aerosol optical depth

In the recent years, concentration of fine particulate matter that are 2.5 microns or less in diameter (PM2.5) in Thailand has consistently exceeded the national ambient air quality standard. Currently, the measurement of PM2.5 concentration relies on air quality monitoring stations operated by the Pollution Control Department of Thailand (PCD). However, these stations are insufficient, particularly in rural areas, where agricultural open burning are major sources of pollution after harvesting period. This study aims to enhance the monitoring of PM2.5 concentration by leveraging cost-effective technologies. We propose the integration of satellite data, specifically Aerosol Optical Depth (AOD) from Multi-Angle Atmospheric Correction (MAIAC) product and Himawari-8 satellites, with the Weather Research and Forecasting Model (WRF) data, to provide supplementary data to the ground-based monitoring. Hourly 5 × 5 km2 AOD data from Himawari-8 were downscaled to a high-resolution of 1 × 1 km2, leveraging the AOD distribution pattern of the concurrent MAIAC product using eXtreme Gradient Boosting (XGBoost) model. Notably, during Thailand’s rainy season (May to August), the study observed a relative reduction in the training model’s R-square value. This phenomenon is attributed to temporal discrepancies between Himawari-8 and the MAIAC products during this period. The predictive models of PM2.5 concentrations with the identification of pertinent variables through Pearson’s correlation analysis and recursive feature elimination, driven by the robust XGBoost model. Subsequently, the downscaled AOD, wind speed, temperature, and pressure were identified as predictors for the estimation of hourly PM2.5 concentration. This comprehensive approach enabled the projection of PM2.5 levels across Thailand, encompassing over 600,000 grids at 1 × 1 km2 resolution. The developed models, thus, offer a valuable tool for robust and high-resolution PM2.5 concentration estimation, presenting significant implications for air quality monitoring and management in Thailand.

THE APPLICATION OF LINEAR REGRESSION AND ARTIFICIAL NEAURAL NETWORKS TO FORECAST THE AMOUNT OF ELECTRONIC WASTE IN THAILAND

This research aims to investigate the input factors influencing e-waste generation and analyze their predictive capabilities using linear regression techniques and neural networks. The study utilizes data collected from the population, Gross Domestic Product (GDP), inflation, and the amount of e-waste obtained from the Pollution Control Department. By comparing the performance of linear regression and neural networks, this research seeks to identify the most effective approach for modeling and forecasting e-waste generation based on the selected input factors. The findings will contribute to improved understanding and prediction of e-waste patterns, aiding policymakers and waste management authorities in developing sustainable strategies for e-waste management. The data were forecast the possible volume of e-waste in the future. A model using neural network modeling techniques be dividing data into different layers:3 input layers,3 hidden layers,1 bias, and 1 output layer.The effect of deep learning is to get a Learning Rate: LR = 0.01 that doesnt increase the loss value. This allows the model to be trained as fast as possible, with a loss of0.0056, Os 92 ms/step. Neural Network learned with data, and reworked it to fit this data 500 times (Epochs = 500), where Root Mean Squared Error: RMSE= 0.0751, RMSE approaches 0, showing that the model is highly accurate.

Performance Evaluation of Low-Cost Particulate Matter Sensors for PM2.5 and PM10 Measurement

This paper presents the performance evaluation of low-cost particulate matter sensors (LCPMS) using light scattering technique. By creating a device for controlling sensors and measuring particulate matter with diameter of less than 2.5 microns (PM2.5) and 10 microns (PM10) for high- and low-concentration environments in a test chamber and for comparing with the average PM2.5 and PM10 of 1-hour and 24-hour continuous of Pollution Control Department (BAM1020) in February to June 2022. From the PM2.5 and PM10 results, it was found that LCPMS and BAM1020 are very strong correlation in the perfect positive. Correlation coefficient square (R2) and measurement error (EM) are decreased by decreasing sampler time of LCPMS detection. The average PM2.5 of 24-hour continuous of LCPMS_5 is very strong correlation with BAM1020 by R2 = 0.7786 and EM = 20.35. The average PM10 of 24-hour continuous of LCPMS_1 is very strong correlation with BAM1020 by R2 = 0.8662 and EM = 13.53.

Association between ambient air particulate matter and human health impacts in northern Thailand

Air pollution in Thailand is regarded as a serious health threat, especially in the northern region. High levels of particulate matter (PM2.5 and PM10) are strongly linked to severe health consequences and mortality. This study analyzed the relationship between exposure to ambient concentrations of PM2.5 and PM10 by using data from the Pollution Control Department of Thailand and the burden of disease due to an increase in the ambient particulate matter concentrations in northern Thailand. This study was conducted using the Life Cycle Assessment methodology considering the human health damage impact category in the ReCiPe 2016 method. The results revealed that the annual average years of life lived with disability from ambient PM2.5 in northern Thailand is about 41,372 years, while from PM10 it is about 59,064 years per 100,000 population. The number of deaths from lung cancer and cardiopulmonary diseases caused by PM2.5 were approximately 0.04% and 0.06% of the population of northern Thailand, respectively. Deaths due to lung cancer and cardiopulmonary diseases caused by PM10, on the other hand, were approximately 0.06% and 0.08%, respectively. The findings expressed the actual severity of the impact of air pollution on human health. It can provide valuable insights for organizations in setting strategies to address air pollution. Organizations can build well-informed strategies and turn them into legal plans by exploiting the study’s findings. This ensures that their efforts to tackle air pollution are successful, in accordance with regulations, and contribute to a healthier, more sustainable future guidelines on appropriate practices of air pollution act/policy linkage with climate change mitigation.

Evaluation the Situation of Heavy Metal Contamination on a Sandy Beach in the Eastern Provinces of Thailand

Thailand's eastern provinces are essential as a hub for industry and tourism, effect to the study has purposed for heavy metal contamination of a beach in the Thai Gulf area in the east of Thailand was monitored and focuses on the use of the enrichment factor (EF) and geoaccumulation index (Igeo) to indicate the environmental condition of beaches. The 30 sample sites were in Chonburi (CHR), Rayong (RY), Chanthaburi (CB), and Trad (TR) provinces, along a sandy beach of about 320 kilometers in length. An inductively coupled plasma technique (ICP-OES) was used to analyze the heavy metals present in the samples. The sand of the range with granulometries greater than 0.85 (18%), between 0.85–0.25 (77%), or less than 0.25 mm (5%). The most common heavy metal found in the samples was Fe at 1632±931 mg/kg dry weight, and the number of heavy metals found in the samples did not exceed the Pollution Control Department of Thailand standards. Principle Component Analysis (PCA) indicated that land use activities influence Hg content. The Igeo of Hg was 1–1.99 (moderately polluted) in sample location 4th of the Rayong province, which has an industrial zone and a port. The EF was mainly within the range of 2–5 in the four provinces studied (indicating deficiency to minimal enrichment), except for one location in Trad and Rayong province, which had an EF of over 5; a possible reason for this is that the area is close to agricultural and aquacultural zones, the government organizations can use the data to plan, monitor, and promote tourism in the future.

Production and characteristics of refuse derived fuel from open-dump solid waste

The objective of this study was to explore the utilization of open-dump solid waste by refuse-derived fuel (RDF) production. Eight RDF samples with different composition percentages were produced. The experimental results indicated that bulk density of eight RDF samples including RDF-A, RDF-B, RDF-C, RDF-D, RDF-E, RDF-F, RDF-G and RDF-H yield in range of 118–259 kg/m3. These were comparable to some kinds of biomass such as sawdust, soft wood chips and baled straw. Heating value, moisture content, volatile matter, ash residue, and fixed carbon were in the range of 28.34–30.93 MJ/kg, 0.00–1.47 %, 85.55–90.71 %, 5.97–10.02 % and 2.53–5.06 %, respectively. Metal compositions, such as Cr, Cu, Zn, Mn and Pb yielded lower values than values of the European RDF quality standard. For the experimental combustion results, it was shown that the emissions of CO, SO2, NO, and HCl were below the standard criteria of the Pollution Control Department.

Effects of sediment resuspension and changes in water nutrient concentrations on the remobilization of lead from contaminated sediments in Klity Creek, Thailand

As Pb-containing sediments in Klity Creek have had negative impacts on the area for more than 20 years, the Supreme Court ordered the Pollution Control Department (PCD) of Thailand to remediate the site. In response to the court order, the PCD decided to reduce the contamination level by dredging the sediments of the creek. Therefore, this study is the first investigation to be conducted on the coupled effects of sediment resuspension caused by dredging and changes in water nutrient concentrations upon the remobilization of Pb from sediments into the water column. The Pb concentrations and speciation in both the water and sediments collected from upstream and downstream regions of the contaminated area were determined. The results showed that the total Pb concentrations in the water taken from all sampling sites in both the dry and wet seasons were lower than the national standard (50 μg/L), and a very low mobility index was found for Pb. The highest total Pb concentration in the sediments (6930 mg/kg) from the downstream site was 23.7- to 30.4-fold greater than those of the sediments collected from the upstream site. The predominant Pb species (organic and residual Pb fractions) in the sediments collected during the dry season were identified. However, carbonate- and Fe–Mn oxide-bound Pb fractions were mainly found in the sediments collected in the wet season. The diffusive gradients in thin films (DGT)-labile Pb concentrations, which reached 2.1 mg/L, indicated potential toxicity to aquatic organisms. A total of nine resuspension scenarios generalizing all changes in water nutrient concentrations in addition to sediment resuspension due to dredging were constructed. The results confirmed that sediment resuspension alone could remobilize Pb from the sediments into the water at levels from 0.06 to 16.9 μg/L. Sediment resuspension in water contaminated with 1 mg/L phosphate (PO43−) led to the dissolution of 28.4–73.0 μg/L Pb in the water column. Nitrate (NO3−) did not significantly remobilize Pb from the sediments into the water. The high ionic strength and activity coefficient of PO43− in the water were expected to cause the retention of dissolved Pb in the water and enhance the remobilization of Pb from the sediments due to the association of Pb with PO43− in the water.

Estimating the Mean of PM2.5 with Missing Data in the Area Around Electricity Generating Authority of Thailand Using the Improved Compromised Imputation Method

Particulate matter with an aerodynamic diameter of less than 2.5 m or , is one of the air pollutants that has been found to be at unsafe levels for a number of years in Thailand, leading to public health concerns. In order to lessen the detrimental effects of air pollution, monitoring and analysis of concentration are crucial. Following the study of data from the Pollution Control Department Report in the area around the Electricity Generating Authority of Thailand in January 2019, it was found that there was data missing in the information. It is well-known that missing data can reduce the accuracy of data analysis. To solve the missing data problem, this paper proposes an improved method of compromised imputation and a corresponding resultant estimator to deal with estimating the mean of concentrations in the area. The bias and mean square error of the estimator obtained from the proposed method were derived. The conditions which favor the performance of our estimator over other estimators obtained from the mean, ratio, and compromised imputation methods were obtained using mean square error to apply in the area. The mean of concentrations in this case using the proposed estimator was equal to 47.13 g/m3, indicating that it did not exceed unsafe levels (&lt;50 g/m3) under certain conditions. In order to support more accurate data analysis that will lead to effective management of air pollution problems in the future, this research proposes a new method that is more effective than the existing methods under missing data problem.

Estimation of PM2.5 surface concentrations in the North-eastern and Eastern regions of Thailand from NASA’s MERRA2 satellite data

The study aims to monitor PM2.5 concentrations in the north-eastern and eastern regions of Thailand for the past 5 years (from 2017 to 2021) from satellite observations for monitoring air quality. To ensure the PM2.5 concentrations from satellite data of NASA’s MEERA2 products, it is necessary to evaluate and analyze the relationship between PM2.5 data from satellite observations and higher precision data from ground-based stations of the Pollution Control Department (PCD) in terms of various statistical values, such as squared correlation coefficient (R2), mean (Bias) and Root Mean Square Error (RMSE). Then, the estimation of the monthly average for PM2.5 concentrations was performed using satellite data of MERRA2 products by bias correction method with twenty PCD’s ground stations. The result shows that before correcting the relationship value is relatively low, the performance of estimating PM2.5 concentrations from satellite data needs to be improved from the monthly average using the bias correction method. After bias-corrected, a correlation coefficient (R2) value of 0.73 was improved from 0.56. A mean (Bias) of 0.03 µg/m3 and an RMSE of 6.39 µg/m3 were decreased. It is clear that bias correction can help to improve estimating PM2.5 concentrations from MEERA2 products.

Assessment of health burden due to the emissions of fine particulate matter from motor vehicles: A case of Nakhon Ratchasima province, Thailand

Air pollution, owing to the ever-increasing transport vehicle fleet, and adverse health effects are increasing in provinces of Thailand. The study estimated that the vehicle fleet size of Nakhon Ratchasima (NR) province of Thailand will grow to 2 million vehicles by 2030, which was 1.36 million in 2021. In NR, the PM2.5 and PM10 concentrations already surpassed both WHO and NAAQS guidelines in 2019–2021. Using Pollution Control Department (PCD) approved Tier I and II Methodology of EMEP/EEA, this research estimated that the total tailpipe emission load will be 1039 tons of PM2.5, 16,630 tons of NO₂, 20,623 tons of CO, 195 tons NH₃, and 249 tons of SO₂ in NR during 2030. The emission load will increase to 1752 tons of PM2.5, 21,126 tons of NO2, 25,559 tons of CO, 361 tons of NH3 and 9344 tons of SO₂ during 2030 if upstream emissions are considered. This study has developed five control scenarios in line with the directives of PCD to mitigate the adverse health from vehicle-led air pollution in NR and implementation during 2024–2030.According to the study, different control scenarios to be implemented during 2024–2030, will be able to keep the fleet size of vehicles in the NR under control. The results show that the control scenarios will keep the annual tailpipe emission of PM2.5 at 604 tons in 2030, a 42 % reduction over the 2030 Business-As-Usual scenario (BAU). The health damage in the range of 6941 to 11,625 disability-adjusted life years (DALYs) under the 2030 BAU scenario in NR due to tailpipe and upstream emissions can be reduced to 4162–7318 DALYs with the implementation of different control scenarios. The control scenarios will also provide significant economic benefits ranging from 4465 to 6718 million THB during 2024–2030 through reduced DALYs and associated costs.

Relationship between Potential Trace Elements Contamination in Sediment and Macrofauna in the Upper Gulf of Thailand.

The relationship between heavy metal contamination in sediment and macrofauna in the upper Gulf of Thailand is presented as an indication of the environmental quality of coastal waters. This study aims to monitor five heavy metal elements (As, Cd, Mn, Ni, and Pb) between sediment and the sandworm (Perinereis quatrefagesi (P. quatrefagesi)). The geoaccumulation index (I geo), enrichment factor (EF), and contamination factor (CF), including the pollution load index (PLI), were used for statistical analyses by one-way analysis of variance (ANOVA), and differences in the data were compared using the least significant difference (LSD) test. The sediment heavy metal concentrations were found to decrease (Al > Mn > Pb > Ni > Cd > As), and the quantity of heavy metal contamination in the sediment was not over the emergency point defined by the Pollution Control Department in Thailand. The Mn at the SH and SP points has I geo > 0 but <1 and is considered not polluted to moderately polluted. The EF overall is <2 and is deficient in mineral enrichment. The CF of the Samut Sakhon (SH) and Samut Prakan (SP) sites had high Al and Mn values, indicating a low pollution level, but the PLI had an all-site average of 0.0215 ± 0.0252, which is <1. This indicates that the present areas are close to ideal and not polluted. To measure heavy metals in macrofauna, P. quatrefagesi was assessed, and concentrations were found to decrease (Mn > Ni > Cd > Pb > As). The Mn between sediment and the sandworm was directly correlated (r = 0.976; p < 0.01). The sandworm performs as a bioindicator for the quality of coastal sediment, specifically with Mn; thus, the result present at a baseline level may grow in the future if there is no control measure for defensive measures.

Water Quality Analysis for Agriculture: A Case Study of Nong Khon Saen Community Area, Udon Thani, Thailand

This work aims to assess the water quality in the Nong Khon Saen community area in Udon Thani for agriculture purposes and its suitability for direct use in water consumption. The sample was collected annually from January to March during the period 2019–2022. The work was undertaken to investigate two different water resources (Huai Luang River and Nong Khon Saen reservoir) for the parameters of pH, temperature, electrical conductivity (EC), dissolved oxygen (DO), and biochemical oxygen demand (BOD). The results showed that all parameters of Huai Luang River were within the acceptable limits of Type 3 surface water quality standards from the Pollution Control Department (PCD), Thailand. The average pH was 6.59±0.18 (Std. pH 5–9), temperature was 27±0.01 ℃ (Std. Natural), EC was 679±12.11 μS/cm (Std. None), DO was 5.39±0.63 mg/l (Std. &gt; 4.0 mg/L), and BOD was 1.71±0.70 mg/L (BOD Std. &lt; 2.0 mg/l). The acceptable water quality parameters were caused by general flushing of rainfall, river water flow, and runoff from agricultural fields. Moreover, the water quality of the Nong Khon Saen reservoir showed a higher BOD concentration (2.61±0.54 mg/L) beyond acceptable limits. The high concentration of BOD was caused by the water plants and the decomposition of animals in the surface water area. For other parameters, the average pH (7.11±0.42), Temperature (28±0.01 ℃), EC (689.58±24.18 μS/cm) and DO (8.39±1.13 mg/L) were shown within acceptable limits. Therefore, the water quality is a requirement concerned with agricultural activities that are dependent on water resources.

Assessment of Surface Water Quality of Huai Luang River in Udon Thani, Thailand, Using the Water Quality Index

This study aimed to analyze the surface water quality of the Huai Luang River using the water quality index (WQI) method. Five parameters were analyzed to assess the water quality of the Huai Luang River; they were dissolved oxygen (DO), biochemical oxygen demand (BOD), total coliform bacteria (TCB), fecal coliform bacteria (FCB), and ammonia (NH3). The Huai Luang River is the municipality’s primary consumption water source in Udon Thani province, Thailand. WQI classifications were calculated by using the surface water quality standards program of Thailand’s Pollution Control Department (PCD-WQI). The results showed that the water quality in the Huai Luang River was bad status, in the WQI range of 54-60 scores. The major sources of pollutants in the Huai Luang River were wastewater from communities, agriculture, and animal farms are the main contributors to water pollution. Seasonal water quality variations were observed at each monitoring site from March 2022 to February 2023. WQI concentration in the rainy season (March to June) was generally among the highest (57 score) compared with other seasons (56 score in winter and 54 score in summer). While the WQI concentration of the upstream sampling site (domestic wastewater) had the lowest (55 score) compared with the other sites (59 score for midstream and 60 score for downstream). Therefore, this research study presented valuable information on WQI for the Huai Luang River for water quality management by administrators and local community participation.