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Near-road fine particulate matter concentration estimation using artificial neural network approach
Zhang, D.Z. & Peng, Z.R.
Abstract
Evidence has shown a strong association
between ambient particulate matter and adverse health
problems. In urban areas, most of households are located
near arterial roads, which are exposure to fine particulate
matter directly. Hence, it is critical to understand the nearroad
fine particulate matter concentration and distribution
for the purpose of health risk analysis. This paper applies
artificial neural network to estimate the near-road fine
particulate matter concentration. Factors influencing the
detected concentration are classified into four categories:
traffic-related, weather-related, detection location-related
and background-related. The estimated values are compared
with concentrations detected by monitoring campaigns
in Gainesville, FL and Shanghai, China.
Distinguished from previous research, this study illustrates
the fine particulate matter dispersion and distribution
within 50 m near road with portable fine particulate matter
detectors and weather instruments. The results indicate that
artificial neural network approach is capable of producing
accurate estimation of pollutant dispersion near road.
Besides, fine particulate matter concentration decayed
about a half at 30 m distance from an arterial road in
Gainesville, FL. Background contributes to more than 2/3
of the detected value at roadside in Shanghai, and the
distance–decay pattern is not as obvious as that in
Gainesville, which is different from previous studies
reported in the literature. An artificial neural network
model performs better after removing the background
concentration and with higher concentration value of fine
particulate matter.
Keywords
Fine particulate matter; Artificial neural network; Dispersion prediction model; Monitoring campaign
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