Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes.

Chao-Yuan Lin,Cheng-Yu Lin,Mon-Ling Chiang

doi:10.3390/ijerph13060553

Chao-Yuan Lin, Cheng-Yu Lin + Show 1 more

Open Access

https://doi.org/10.3390/ijerph13060553

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Abstract

Around the estuary of the Zhuo-Shui River in Taiwan, the waters recede during the winter, causing an increase in bare land area and exposing a large amount of fine earth and sand particles that were deposited on the riverbed. Observations at the site revealed that when northeastern monsoons blow over bare land without vegetation or water cover, the fine particles are readily lifted by the wind, forming river dust, which greatly endangers the health of nearby residents. Therefore, determining which factors affect river dust and constructing a model to predict river dust concentration are extremely important in the research and development of a prototype warning system for areas at risk of river dust emissions. In this study, the region around the estuary of the Zhuo-Shui River (from the Zi-Qiang Bridge to the Xi-Bin Bridge) was selected as the research area. Data from a nearby air quality monitoring station were used to screen for days with river dust episodes. The relationships between PM10 concentration and meteorological factors or bare land area were analyzed at different temporal scales to explore the factors that affect river dust emissions. Study results showed that no single factor alone had adequate power to explain daily average or daily maximum PM10 concentration. Stepwise regression analysis of multiple factors showed that the model could not effectively predict daily average PM10 concentration, but daily maximum PM10 concentration could be predicted by a combination of wind velocity, temperature, and bare land area; the coefficient of determination for this model was 0.67. It was inferred that river dust episodes are caused by the combined effect of multiple factors. In addition, research data also showed a time lag effect between meteorological factors and hourly PM10 concentration. This characteristic was applied to the construction of a prediction model, and can be used in an early warning system for local residents.

Highlights

Particulate matter less than 10 μm in diameter (PM10 ) is defined as respirable particulate matter that can accumulate in the lungs and is harmful to human health [1]
PM10 concentration and meteorological factors or bare land area were analyzed at different temporal scales to explore the factors that affect river dust emissions
Stepwise regression analysis of multiple factors showed that the model could not effectively predict daily average PM10 concentration, but daily maximum PM10 concentration could be predicted by a combination of wind velocity, temperature, and bare land area; the coefficient of determination for this model was 0.67

Summary

Introduction

Particulate matter less than 10 μm in diameter (PM10 ) is defined as respirable particulate matter that can accumulate in the lungs and is harmful to human health [1]. PM10 pollution platforms should be developed to inform the public of harmful air pollution events, as well as to adapt air pollution control strategies [2,3]. Deterministic and statistical models are the two main approaches to the prediction of PM10 [3]. The complexity of deterministic approaches, which require detailed information on physical–chemical processes in the atmosphere and detailed knowledge concerning the emission sources of various pollutants, could lead to inaccurate PM10 prediction for short and medium ranges [2,4]. Statistical models or empirical models, due to their simplicity, are alternative approaches that are frequently used to predict PM10 for complex site-specific solutions with acceptable accuracy [2,3,6,7,8].

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