Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes

River dust-induced air pollution in a changing climate: A study of Taiwan's Choshui and Kaoping Rivers

This study investigates river dust episodes along the Choshui and Kaoping Rivers in Taiwan, focusing on their spatiotemporal distribution and correlation with hydrometeorological factors (temperature, precipitation, relative humidity, and wind speed). Using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm and time-dependent intrinsic correlation (TDIC) analysis, we identified significant annual and diurnal correlations between PM10 concentrations and these factors. The analysis revealed that wind speed at Lunbei station had a positive annual correlation with PM10, while other factors exhibited significant negative correlations. Seasonal variations in PM10 correlations with temperature, relative humidity, and wind speed were observed, aligning with the prevailing seasons of river dust episodes. Wind motion analysis highlighted diurnal associations with land-sea breezes and annual correlations with the winter monsoon. Specifically, the Choshui River's dust events coincided with the northeast monsoon, whereas the Kaoping River's events occurred during the northwest and southwest monsoons. The study also uncovered that downstream stations (Lunbei and Daliao) were more prone to severe dust events than upstream stations (Douliu and Pingtung). These findings enhance our understanding of the dynamics and environmental impacts of river dust episodes, providing valuable insights for air quality management and health risk mitigation.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Spatial distributions of PM10-bound metal elements in the central part of western Taiwan and their potential emission sources and the carcinogenic health risks

This study aimed to investigate the spatial distribution of metal elements in PM10 and their potential sources and associated health risks over a period of two years in eight locations in the central part of western Taiwan. The study revealed that the mass concentration of PM10 and the total mass concentration of 20 metal elements in PM10 were 39.0 μg m-3 and 4.74 μg m-3, respectively, with total metal elements accounting for approximately 13.0% of PM10. Of the total metal elements, 95.6% were crustal elements (Al, Ca, Fe, K, Mg, and Na), with trace elements (As, Ba, Cd, Cr, Co, Cu, Ga, Mn, Ni, Pb, Sb, Se, V, and Zn) contributing only 4.4%. Spatially, the inland areas exhibited higher PM10 concentrations due to lee-side topography and low wind speeds. In contrast, the coastal regions exhibited higher total metal concentrations because of the dominance of crustal elements from sea salt and crustal soil. Four primary sources of metal elements in PM10 were identified as sea salt (58%), re-suspended dust (32%), vehicle emissions and waste incineration (8%), and industrial emissions and power plants (2%). The positive matrix factorization (PMF) analysis results indicated that natural sources like sea salt and road dust contributed up to 90% of the total metal elements in PM10, while only 10% was attributed to human activities. The excess cancer risks (ECRs) associated with As, Co, and Cr(VI) were greater than 1 × 10-6, and the total ECR was 6.42 × 10-5. Although only 10% of total metal elements in PM10 came from human activities, they contributed to 82% of the total ECR.

Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

A total of 188,859 meteorological-PM$$_{10}$$ 10 data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM$$_{10}$$ 10 in two districts of South Lima in Peru, hourly, daily, monthly and seasonal variations of the data were analyzed. Principal Component Analysis (PCA) and linear/nonlinear modeling were applied. The results showed the highest annual average PM$$_{10}$$ 10 for San Juan de Miraflores (SJM) (PM$$_{10}$$ 10 -SJM: 78.7 $$\upmu$$ μ g/m$$^{3}$$ 3 ) and the lowest in Santiago de Surco (SS) (PM$$_{10}$$ 10 -SS: 40.2 $$\upmu$$ μ g/m$$^{3}$$ 3 ). The PCA showed the influence of relative humidity (RH)-atmospheric pressure (AP)-temperature (T)/dew point (DP)-wind speed (WS)-wind direction (WD) combinations. Cool months with higher humidity and atmospheric instability decreased PM$$_{10}$$ 10 values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM$$_{10}$$ 10 at peak times in the morning and evening. The Multiple linear regression (MLR) showed the best correlation (r = 0.6166), followed by the three-dimensional model LogAP-LogWD-LogPM$$_{10}$$ 10 (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE $$<0.3$$ < 0.3 ) and the NSE-MLR criterion (0.3804) was acceptable. PM$$_{10}$$ 10 prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic.

Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area

We implemented a spatial model for analysing PM10 maxima across the Mexico City metropolitan area during the period 1995–2016. We assumed that these maxima follow a non-identical generalized extreme value (GEV) distribution and modeled the trend by introducing multivariate smoothing spline functions into the probability GEV distribution. A flexible, three-stage hierarchical Bayesian approach was developed to analyse the distribution of the PM10 maxima in space and time. We evaluated the statistical model’s performance by using a simulation study. The results showed strong evidence of a positive correlation between the PM10 maxima and the longitude and latitude. The relationship between time and the PM10 maxima was negative, indicating a decreasing trend over time. Finally, a high risk of PM10 maxima presenting levels above 1000 μg/m3 (return period: 25 yr) was observed in the northwestern region of the study area.