PM<sub>2.5</sub> ∕ PM<sub>10</sub> ratio prediction based on a long short-term memory neural network in Wuhan, China
<p>Air pollution is a serious problem in China that urgently needs to be addressed. Air pollution has a great impact on the lives of citizens and on urban development. The particulate matter (PM) value is usually used to indicate the degree of air pollution. In addition to that of PM<span c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-03-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/13/1499/2020/gmd-13-1499-2020.pdf |
| Summary: | <p>Air pollution is a serious problem in China that urgently needs to be
addressed. Air pollution has a great impact on the lives of citizens and on
urban development. The particulate matter (PM) value is usually used to
indicate the degree of air pollution. In addition to that of PM<span class="inline-formula"><sub>2.5</sub></span> and
PM<span class="inline-formula"><sub>10</sub></span>, the use of the PM<span class="inline-formula"><sub>2.5</sub></span> <span class="inline-formula">∕</span> PM<span class="inline-formula"><sub>10</sub></span> ratio as an indicator and
assessor of air pollution has also become more widespread. This ratio
reflects the air pollution conditions and pollution sources. In this paper,
a better composite prediction system aimed at improving the accuracy and
spatiotemporal applicability of PM<span class="inline-formula"><sub>2.5</sub></span> <span class="inline-formula">∕</span> PM<span class="inline-formula"><sub>10</sub></span> was proposed. First,
the aerosol optical depth (AOD) in 2017 in Wuhan was obtained based on
Moderate Resolution Imaging Spectroradiometer (MODIS) images, with a 1 km
spatial resolution, by using the dense dark vegetation (DDV) method. Second,
the AOD was corrected by calculating the planetary boundary layer height
(PBLH) and relative humidity (RH). Third, the coefficient of determination
of the optimal subset selection was used to select the factor with the
highest correlation with PM<span class="inline-formula"><sub>2.5</sub></span> <span class="inline-formula">∕</span> PM<span class="inline-formula"><sub>10</sub></span> from meteorological factors
and gaseous pollutants. Then, PM<span class="inline-formula"><sub>2.5</sub></span> <span class="inline-formula">∕</span> PM<span class="inline-formula"><sub>10</sub></span> predictions based on
time, space, and random patterns were obtained by using nine factors (the
corrected AOD, meteorological data, and gaseous pollutant data) with the long
short-term memory (LSTM) neural network method, which is a dynamic model
that remembers historical information and applies it to the current output.
Finally, the LSTM model prediction results were compared and analyzed with
the results of other intelligent models. The results showed that the LSTM
model had significant advantages in the average, maximum, and minimum
accuracy and the stability of PM<span class="inline-formula"><sub>2.5</sub></span> <span class="inline-formula">∕</span> PM<span class="inline-formula"><sub>10</sub></span> prediction.</p> |
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| ISSN: | 1991-959X 1991-9603 |