Estimation of surface soil moisture content using fractals.

Soil moisture content is a key factor affecting surface evaporation in the hydrologic cycle, with application in water resources management, early drought warning system, irrigation management, and estimation of crop yield. The subject of soil moisture content has long been of interest in hydrology, agriculture, forestry, and soil mechanics engineering. The cracking of surface soil due to dryness is often used to describe the drought condition of the land based on the certain relationship between soil cracking and moisture content. This study establishes the relation between fractal dimensions of soil cracking patterns and moisture content of surface soil. Sixteen soil samples were prepared and subjected to drying under different temperature levels to crack in the laboratory. Photographs of the cracks were digitized and an algorithm for calculating fractal dimensions of cracking patterns was developed. The results demonstrated that the fractal dimensions of cracking patterns are highly correlated with the surface soil moisture content; the lower the soil moisture content, the larger the fractal dimension is. Therefore, the surface soil moisture content-fractal dimension (DB-w) rating curves can be established. Once crack structures occur on surface soil, the moisture content of the surface soil can be estimated quickly and accurately by mean of remote sensing technique such as UAV together with the fractal analysis.

Emergency room visits associated with particulate concentration and Asian dust storms in metropolitan Taipei.

This study evaluated risks of emergency room visits (ERV) for all causes, circulatory diseases, and respiratory diseases associated with concentrations of particulate matter (PM10 and PM2.5) and Asian dust storms (ADS) from 2000 to 2008 in metropolitan Taipei. Cumulative 4-day (lag 0-3) relative risks (RR) and confidence intervals (CI) of cause-specific ERV associated with daily concentrations of PM10 or PM2.5 and ADS based on study period (ADS frequently inflicted period: 2000-2004 and less-inflicted period: 2005-2008) were estimated using a distributed lag non-linear model with Poisson distribution. Risks associated with ADS-inflicted season (winter and spring), strength (ratio of stations with Pollutant Standard Index above 100 is < 0.5 or ≥ 0.5), and duration (ADS lasting for 1-3 days or ≥ 4 days) were especially evaluated. In non-linear models, an increase in PM10 from 10 µg/m(3) to 50 µg/m(3) was associated with increased risk of ERV for all causes and respiratory disease with cumulative 4- day RR of 1.18 (95% CI: 1.13, 1.24) and 1.37 (95% CI: 1.23, 1.54), respectively. From 2005 to 2008, the cumulative 4-day RR for an ERV related to an increase in PM2.5 from 5 µg/m(3) to 30 µg/m(3) is 1.21 (95% CI: 1.03, 1.41) for respiratory diseases, and 1.15 (95% CI: 1.08, 1.22) for all causes. In comparison with normal days, elevated ERV of all causes and respiratory diseases was also associated with winter ADS (with corresponding RRs of 1.10 (95% CI: 1.07, 1.13) and 1.14 (95% CI: 1.08, 1.21)) and shorter and less area-affected ADS (with corresponding RRs of 1.07 (95% CI: 1.01, 1.10) and 1.09 (95% CI: 1.03, 1.14)) from 2000 to 2004. Results of this study demonstrate that population health risk varies not only with PM concentration, but also with the ADS characteristics.

Estimation of river pollution index in a tidal stream using kriging analysis.

Tidal streams are complex watercourses that represent a transitional zone between riverine and marine systems; they occur where fresh and marine waters converge. Because tidal circulation processes cause substantial turbulence in these highly dynamic zones, tidal streams are the most productive of water bodies. Their rich biological diversity, combined with the convenience of land and water transports, provide sites for concentrated populations that evolve into large cities. Domestic wastewater is generally discharged directly into tidal streams in Taiwan, necessitating regular evaluation of the water quality of these streams. Given the complex flow dynamics of tidal streams, only a few models can effectively evaluate and identify pollution levels. This study evaluates the river pollution index (RPI) in tidal streams by using kriging analysis. This is a geostatistical method for interpolating random spatial variation to estimate linear grid points in two or three dimensions. A kriging-based method is developed to evaluate RPI in tidal streams, which is typically considered as 1D in hydraulic engineering. The proposed method efficiently evaluates RPI in tidal streams with the minimum amount of water quality data. Data of the Tanshui River downstream reach available from an estuarine area validate the accuracy and reliability of the proposed method. Results of this study demonstrate that this simple yet reliable method can effectively estimate RPI in tidal streams.