Environmental Protection Administration

Abstract East Asian monsoon shows strong annual cycle with the wet season in summer and dry in winter. In addition to annual cycle, some monsoon regions exhibit distinct climatological sub-seasonal variations such as fast annual cycle, climatological intraseasonal oscillations (CISOs) and climate singularities. In this paper a significant cyclic-type outgoing longwave radiation (OLR) singularity during the spring to summer transition period over the South China Sea (SCS) is identified based on the daily OLR data from 1979 to 2023. The quasi-CISO in each individual year is invented as an analogue to CISO. Its relationship with Madden–Julian Oscillation (MJO) and the SCS summer monsoon onset (SCSSM) is analyzed. The quasi-CISO shows large interannual variability. A group of years with the quasi-CISO amplitudes larger than the medium of all sample years are selected to form the quasi-CISO mode. Among them 72% of the quasi-CISO cyclic valley coincides with the SCSSM onset time represented by the persistent intensification of the monsoonal westerlies, which are the SCS-CISO years. The MJO activity during the sixteen SCS-CISO years shows three different levels. Composite results of eight SCS-CISO years with strong MJO show clear synchronized planetary- and regional-scale convection and circulation progressions where strong MJOs over the equatorial Indian Ocean appeared two weeks before SCSSM onset. Strong convection over the SCS associated with the SCSSM onset process can be triggered by the enhanced low-level southerly flow by MJO’s subsidence leg over the equatorial maritime continent through increasing the moisture transport. This study provides observational-based evidence that supports earlier findings about CISO, CMJO and SCSSM onset. The findings can be applied to evaluate the Sub-seasonal to Seasonal forecast model products for better understanding of the extended-range weather and climate predictability.

Modeling environmental concentrations of plant protection products typically includes runoff, drainage, and leaching processes, which are well represented in recent landscape scale modeling approaches. However, the modeling of spray drift at the landscape scale is challenging and often simplified or neglected due to high computational efforts. For example, spray drift is often implemented by external calculation of drift curves with the pesticide load added directly to the channel network. Although this approach enables in general a basic landscape-level spray drift estimation, it lacks the spatio-temporal details such as the distribution of drift in relation to other landscape elements (e.g., water bodies, non-target areas). To address these limitations, we developed the Droplet and Atmospheric Dispersion drift (DAD-drift) model which integrates mechanistic droplet model, a micrometeorological model, and a three-dimensional Gaussian puff model designed for ground application. DAD-drift considers the physical principles of spray drift, the spatial relationship between application areas and to non-target areas, as well as local weather conditions at the landscape scale. Its modular design allows for easy integration with other models.We combined, a high-resolution SWAT+ model of an agriculturally dominated catchment in Germany with DAD-drift to enhance our understanding of pesticide transport pathways and to assess the different exposure routes of plant protection products. Flow observation data are used for hard calibration, supported by additional soft calibration data (i.e., evaporation, surface runoff, subsurface drainage, groundwater recharge, total runoff). Agricultural practices, i.e. crop rotations with catch crops, tillage operations, and plant protection product application timing are adopted from a 5-year data set from 2019 to 2023. Results indicated that transport via spray drift is significant for exposure at the landscape scale, with the dominant transport pathway varying considerably based on individual substance properties and application timing.The model setup can be used to identify critical source areas and to optimize the application of plant protection products. In addition, the effectiveness of risk mitigation practices, such as drift reduction nozzles and no-spray buffer strips, can be assessed. Furthermore, linking the exposure dynamics predicted by the SWAT+ model with effect modeling approaches is feasible.

　　Certain limitations arise when utilizing the Monitoring Efficiency Model (MEMO) and Monitoring and Remediation Optimization System (MAROS) to evaluate monitoring well placement at contaminated sites. MEMO is restricted to one-dimensional groundwater flow analysis, while MAROS can only handle two-dimensional spatial distribution of contaminants. These constraints hinder the ability to account for variability in the three-dimensional spatial distribution of contaminants, leading to suboptimal monitoring well configurations. In particular, factors such as geological heterogeneity and contaminant characteristics (e.g., biodegradation, chemical degradation, and physical adsorption) may lead to contaminant omissions or inappropriate monitoring well density distribution, ultimately limiting the efficiency and accuracy of monitoring well placement.　　To address these challenges, this study proposes an optimized approach for monitoring well placement at three-dimensional groundwater contamination sites. The method integrates Bayesian Model Averaging (BMA) and Bayesian Maximum Entropy (BME) to delineate contaminant plumes more accurately and provide optimal recommendations for monitoring well placement. BMA, utilizing Markov Chain Monte Carlo (MCMC) simulations and Bayesian inference, calculates the posterior distribution of multiple potential Conceptual Site Models (CSMs) by evaluating discrepancies between observed and simulated contaminant concentrations.　　Using the weighted CSM, the relative positions between existing monitoring wells and the contaminant plume can be evaluated. During the numerical simulation process, virtual observation points are added to enhance the richness and completeness of data distribution within the contaminated area, further improving the interpolation accuracy of BME. Through this improvement, BME can integrate simulated data with existing monitoring data to precisely predict the locations of additional monitoring wells, supplement critical monitoring data, and optimize the overall monitoring well placement strategy.　　Additionally, this study incorporates monitoring well-installation costs, the value of information (VOI), and trans-information entropy (TE) into a multi-objective optimization framework. By minimizing the objective function, Pareto-optimal solutions are obtained. The Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) is then applied to rank these solutions, enabling decision-makers to balance monitoring efficiency with cost considerations and implement flexible and effective monitoring configurations. It also verifies the feasibility of retaining a significant portion of critical monitoring information through VOI-based quantitative analysis, even with a reduced number of monitoring wells.　　The proposed optimization method has been validated through numerical simulations, demonstrating improved model accuracy under complex site conditions. The results offer adaptable, site-specific solutions that maximize both monitoring efficiency and economic viability. Keywords: Bayesian Model Averaging, Bayesian Maximum Entropy, groundwater contaminant transport, optimization of monitoring well placement

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Abstract Trace residues of organochlorine pesticides (OCPs) in estuarine surface sediments were investigated at three protected wetlands in southern Taiwan using high‐resolution gas chromatography/high‐resolution mass spectrometry (HRGC/HRMS) and gas chromatography‐electron capture detector (GC‐ECD). This study facilitates the development of new strategies for investigating OCPs, particularly at background levels. Linear relationships were obtained between total OCP concentrations (∑OCP), as analyzed by HRGC/HRMS, and the sediment's total organic carbon (TOC) and water content. It contrasted with the results acquired by GC‐ECD, where no significant relationship was found. GC‐ECD is a rugged option for daily routine practice, particularly in cases of patterns yielded by GC‐ECD is clear; the HRGC/HRMS method provides more reliable qualitative and quantitative capabilities and is highly recommended for studying the fate of OCPs and carrying out risk assessments. The average ∑OCP of these three wetlands by HRGC/HRMS was found in a range of 0.214 to 1.049 ng/g dry weight. The highest OCP level might be attributed to associated irrigation systems receiving massive discharges of domestic sewage from an urban area upstream of the wetland. The ratio of dichlorodiphenyltrichloroethane (DDT) to its metabolites indicated that the DDT residue in these areas was from aged input. According to sediment quality guidelines, adverse ecotoxicological effects of OCPs upon sediments were not expected in these protected wetlands.

Abstract Particulate lead (Pb) is a primary air pollutant that affects society because of its health impacts. This study investigates the source sectors of Pb associated with ambient fine particulate matter (PM2.5) over central-western Taiwan (CWT) with new constraints on the Pb-isotopic composition. We demonstrate that the contribution of coal-fired facilities is overwhelming, which is estimated to reach 35±16 % in the summertime and is enhanced to 57±24 % during the winter monsoon seasons. Moreover, fossil-fuel vehicles remain a major source of atmospheric Pb, which accounts for 12±5 %, despite the current absence of a leaded gasoline supply. Significant seasonal and geographical variations in the Pb-isotopic composition are revealed, which suggest that the impact of East Asian (EA) pollution outflows is important in north CWT and drastically declines toward the south. We estimate the average contribution of EA outflows as accounting for 35±15 % (3.6±1.5 ng/m3) of the atmospheric Pb loading in CWT during the winter monsoon seasons.

In 2019, Taiwan completed its first thorough heavy metal investigation of irrigation canal sediments by this study with the support of Taiwan Environmental Protection Administration. Box-and-whisker plots were used to analyze the sediment distribution and to define metal concentrations. Possible metal pollution sources, the polluted agricultural land, irrigation area, and water sources were also evaluated using spatial analysis to understand the possible causes of sediment pollution. Results showed that the main heavy metal in agricultural land was Cu, found in 77% of contaminated agricultural land sites. Most sites with Cu pollution in sediments were in Taoyuan City and Changhua County. The heavy metals present in the sediment pollution sites in Taoyuan were consistent with those of possible pollution sources upstream, namely, Cr, Cu, and Pb. The main heavy metals in sediment pollution sites in Changhua were Cr, Cu, Ni, Pb, and Zn, whereas those for the polluted agricultural land sites were Cr, Cu, Ni, and Zn, without Pb. The main irrigation water sources in Changhua include drainages and rivers, with some receiving most wastewater pollution mass of release of Changhua, and functions as an irrigation water source with a high release mass in Cr, Cu, Ni and Zn. These findings indicate that the sites of sediment pollution, sites of polluted agricultural land, and the sources of pollution share corresponding heavy metal characteristics. Therefore, in Changhua, the sediments were polluted mainly because (1) the irrigation canals received the highest masses of pollutant releases into drainage wastewater of the county; and (2) the return flow from irrigation and the illegal discharge of wastewater. The preliminary assessment results for sediment pollution in Taoyuan also suggest that the main causes may be irrigation by polluted rivers or drainages and return flows.

Identifying and quantifyingchromium in water are important forthe protection of preciouswater resources from chromium pollution. Standard methods howeverare unable to easily distinguish toxic hexavalent chromium, Cr(VI),from innocuous trivalent chromium, Cr(III), are time-consuming, orrequire large sample quantity. We show in this report that Cr(VI)and Cr(III) in water can be differentiated based on their distinctspectral features of surface-enhanced Raman scattering (SERS). TheirSERS signals exhibit different pH dependences: the SERS features ofCr(VI) and Cr(III) are most prominent at pH values of 10 and 5.5,respectively. The obtained limit of detection of Cr(VI) in water isbelow 0.1 mg/L. Both concentration curves of their SERS signals showLangmuir sorption isotherm behavior. A procedure was developed toquantify Cr(VI) concentration based on the direct retrieval or additionmethod with an error of 10%. Finally, the SERS detection of Cr(VI)is shown to be insensitive to co-present Cr(III). The developed SERSprocedure offers potential to monitor toxic chromium in fields.