A carregar...
Publicação
Air Quality Management in Macao – assessment, development of an operational forecast, and future perspectives
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 10.42 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
A combination of assessment, operational forecast, and future perspective was
thoroughly explored to provide an overview of the existing air quality problems in
Macao. The levels of air pollution in Macao often exceed those recommended by the
World Health Organization (WHO). In order for the population to take precautionary
measures and avoid further health risks during high pollution episodes, it is important
to develop a reliable air quality forecast. Statistical models based on linear multiple
regression (MLR) and classification and regression trees (CART) analysis were
successfully developed for Macao, to predict the next day concentrations of NO2,
PM10, PM2.5, and O3.
Meteorological variables were selected from an extensive list of possible variables,
including geopotential height, relative humidity, atmospheric stability, and air
temperature at different vertical levels. Air quality variables translate the resilience of
the recent past concentrations of each pollutant and usually are maximum and/or the
average of latest 24-hour levels. The models were applied in forecasting the next day
average daily concentrations for NO2 and PM and maximum hourly O3 levels for five
air quality monitoring stations. The results are expected to support an operational air quality forecast for Macao.
The work involved two phases. On a first phase, the models utilized meteorological
and air quality variables based on five years of historical data, from 2013 to 2017. Data
from 2013 to 2016 were used to develop the statistical models and data from 2017 was
used for validation purposes. All the developed models were statistically significantly
valid with a 95% confidence level with high coefficients of determination (from 0.78
to 0.93) for all pollutants. On a second phase, these models were used with 2019
validation data, while a new set of models based on a more extended historical data
series, from 2013 to 2018, were also validated with 2019 data. There were no significant
differences in the coefficients of determination (R2
) and minor improvements in root
mean square errors (RMSE), mean absolute errors (MAE) and biases (BIAS) between
the 2013 to 2016 and the 2013 to 2018 data models. In addition, for one air quality
monitoring station (Taipa Ambient), the 2013 to 2018 model was applied for two days
ahead (D2) forecast and the coefficient of determination (R2
) was considerably less
accurate to the one day ahead (D1) forecast, but still able to provide a reliable air quality
forecast for Macao.
To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high
pollution episode for PM2.5 and O3 during 2019, and a low pollution episode during
2020. Regarding the high pollution episode, the period of the Chinese National
Holiday of 2019 was selected, in which high concentration levels were identified for
PM2.5 and O3, with peaks of daily concentration for PM2.5 levels exceeding 55 μg/m3
and the maximum hourly concentration for O3 levels exceeding 400 μg/m3
. For the
low pollution episode, the 2020 period of implementation of the preventive measures
for COVID-19 pandemic was selected, with a low record of daily concentration for
PM2.5 levels at 2 μg/m3 and maximum hourly concentration for O3 levels at 50 μg/m3
.
The 2013 to 2018 model successfully predicted the high pollution episode with
high coefficients of determination (0.92 for PM2.5 and 0.82 for O3). Likewise, the low
pollution episode was also correctly predicted with high coefficients of determination
(0.86 and 0.84 for PM2.5 and O3, respectively). Overall, the results demonstrate that
the statistical forecast model is robust and able to correctly reproduce extreme air
pollution events of both high and low concentration levels.
Machine learning methods maybe adopted to provide significant improvements
in combination of multiple linear regression (MLR) and classification and regression tree (CART) to further improve the accuracy of the statistical forecast. The developed
air pollution forecasting model may be combined with other measures to mitigate the
impact of air pollution in Macao. These may include the establishment of low
emission zones (LEZ), as enforced in some European cities, license plate restrictions
and lottery policy, as used in some Asian, tax exemptions on electric vehicles (EVs)
and exclusive corridors for public transportations.
Descrição
Palavras-chave
Air pollution Particulate Matter Ozone Macao Statistical air quality forecast Pollution episodes