key: cord-1031575-3l7fel0v
authors: Lei, Man Tat; Monjardino, Joana; Mendes, Luisa; Gonçalves, David; Ferreira, Francisco
title: Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19
date: 2020-07-15
journal: Int J Environ Res Public Health
DOI: 10.3390/ijerph17145124
sha: 31ae9fad6279461caf878f107ef0eebef831312b
doc_id: 1031575
cord_uid: 3l7fel0v

Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO(2)), particulate matter (PM(10)), PM(2.5), but not for ozone (O(3)) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R(2)), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). 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 PM(2.5) and O(3) during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM(2.5) and O(3), with peaks of daily concentration exceeding 55 μg/m(3) and 400 μg/m(3), respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM(2.5) and 0.82 for O(3)). The low pollution episode for PM(2.5) and O(3) was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM(2.5) levels at 2 μg/m(3) and O(3) levels at 50 μg/m(3), respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM(2.5) and O(3) with a high coefficient of determination (0.86 and 0.84, 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.

The development of air quality forecast models is essential for cities with high population density, including Macao, one of the most densely populated cities in the world. It is extremely important to predict pollution episodes so the authority can provide a warning to the local community in advance to avoid the adverse air quality, which may lead to severe health consequences. In order to predict

The air quality and meteorological variables that were considered to build all of the air quality statistical models were obtained from Macao Meteorological and Geophysical Bureau (SMG). The air quality data was gathered from the air quality monitoring network, namely for: Macao Roadside, Macao Residential, Taipa Ambient, Taipa Residential, and Coloane Ambient stations, which have a suitable historic dataset of surface air quality measurements for the levels of NO 2 , PM 10 , PM 2.5 , and O 3 concentrations. These background stations (residential and ambient) can capture the regional contribution of PM 10 and PM 2.5 . There is a higher population and traffic density in Macao Roadside and Macao Residential, which are located in the main peninsula, in comparison to Taipa Ambient, Taipa Residential, and Coloane Ambient stations, which are located on the outlying islands.

Meteorological data was obtained from surface observations at SMG's Taipa Grande Meteorological Station, hourly observations from automatic weather stations, such as temperature, relative humidity, precipitation, average wind speed, and dew point temperature, as well as upper-air observations (from Hong Kong King's Park location) such as geopotential heights, thickness, stability, temperature, relative humidity, and dew point temperature at various altitudes. In the present work, statistical models such as multiple linear regression (MLR), and classification and regression tree (CART), are developed, based on historical measurements of meteorological and air quality variables. Table 1 presents all the variables considered as predictors in the MLR and CART forecast models, as shown in previous work [22] . The air quality variables considered included the levels of NO 2 , PM 10 , PM 2.5 , and O 3 MAX concentration from 00:00 to 23:00 of the previous day, two days and three days ago, and from 16:00 of the previous day and 15:00 of today. The meteorological variables being considered included the upper-air observations from King's Park location, Hong Kong Observatory, surface observations and other variables from the monitoring network of Macao Meteorological and Geophysical Bureau (SMG). Table 1 . Variables considered as predictors in the multiple linear regression (MLR) and classification and regression tree (CART) models in all of the air quality forecast models. The results showed that the model for the 2013 to 2018 period was the one that performed best in predicting next-day concentrations levels in 2019, with high R 2 between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants) and low RMSE, MAE, and BIAS. The additional two years of data helped to improve the air quality forecasting model. Nevertheless, with the two other models (2013-2016 and 2015-2018) a significant R 2 (between 0.78 and 0.89 for all pollutants) was also obtained, but it translated into a less reliable air quality forecast.

Regarding model performance indicators obtained per pollutant and station, the majority of models show a good agreement and a similar R 2 range values (from 0.81 to 0.89), except for O 3 MAX , which is more difficult to predict. MLR was used for all pollutants, while CART analysis was used in almost all the O 3 MAX models (Tables 2 and 3 ). This CART analysis complement was an approach to obtain improved results, mainly regarding a better prediction of high pollutant levels. Table 4 presents the final model equations obtained for each pollutant, per air quality monitoring station, in the 2013 to 2018 model. Additionally, the final equations used to predict the levels of NO 2 , PM 10 , PM 2.5 , and O 3_MAX concentrations are presented in Table 4 . 

Taipa Ambient is the representative background location for Macao, and was chosen to assess the background levels of PM 2.5 and O 3 during the extreme pollution episode.

The influx of tourists coming to Macao, in light of the Chinese National Holiday, contributed to an high pollution episode that occurred during late September and early October 2019, with peak daily levels of PM 2.5 concentration exceeding 55 µg/m 3 and O 3 MAX levels exceeding 400 µg/m 3 , largely exceeding the threshold level recommended by the WHO. The levels of PM2.5 and O3 MAX concentrations for Taipa Ambient during the Chinese National Holiday in 2019 (from September to November) are presented in Figures 1 and 2 . Figures 1 and 2 showed the comparison of daily average PM2.5 and O3 MAX concentration during 2018 and 2019, from a month before in September and a month after in November of the Chinese National Holiday. The pollution episode of 2019 occurred just before and going well into the period of Chinese National Holiday (1 to 7 October). As shown in Figures 1 and 2 , the levels of PM2.5 and O3 MAX concentration peaked immediately before, and during, the Chinese National Holiday in late September and early October 2019. The monthly mean concentration of PM2.5 (from September to November) during the Chinese National Holiday in 2019 was 19 μg/m 3 , 24 μg/m 3 , and 28 μg/m 3 , respectively. In addition, the monthly mean concentration of O3 MAX (from September to November) during the Chinese National Holiday in 2019 was 181 μg/m 3 , 163 μg/m 3 , and 172 μg/m 3 , respectively.

The levels of O3 MAX concentrations reached its peak during the late September and early October As shown in Figures 1 and 2 , the levels of PM 2.5 and O 3 MAX concentration peaked immediately before, and during, the Chinese National Holiday in late September and early October 2019. The monthly mean concentration of PM 2.5 (from September to November) during the Chinese National Holiday in 2019 was 19 µg/m 3 , 24 µg/m 3 , and 28 µg/m 3 , respectively. In addition, the monthly mean concentration of O 3 MAX (from September to November) during the Chinese National Holiday in 2019 was 181 µg/m 3 , 163 µg/m 3 , and 172 µg/m 3 , respectively.

The levels of O 3 MAX concentrations reached its peak during the late September and early October due to meteorological factors including predominant winds from the north and east, from the Guangdong Province and Hong Kong, respectively. Temperatures were high in conjunction with low wind speed. The average daily temperature during the ozone peak episode that took place the two-weeks before the Chinese National Holiday (October 1st) was 28 • C, while the maximum daily average was 31 • C. Average wind speed was 2.5 m/s.

Due to the shutdown of nearby industrial sectors during the period of Chinese National Holiday, there were lower emissions of nitrogen oxides associated with the decreased load from the coal power plants in the northern region, usually supporting the operation of the factories. Therefore, this caused a decrease NO x , the precursor of O 3. However, the increase in emissions of VOCs and NOx by vehicles, with chemical reactions in the presence of sunlight, may have caused the peak levels of ozone concentrations under these high temperature favorable conditions.

In contrast, the COVID-19 pandemic has led to the Macao government's decision to temporarily suspend the operation of the casinos and entertainment industry and highly restrict cross border movements, as a preventive measure to reduce population mobility within the region of Macao. As a result, it has caused a low pollution episode during late January and early February 2020, with daily levels of PM 2.5 concentration reaching a record low at 2 µg/m 3 and O 3 MAX levels at 50 µg/m 3 . The reduction of population mobility, and consequently, of traffic emissions in Macao and its nearby Guangdong Province, lead to this lowest PM 2.5 concentration levels.

As shown in Figure 3 , the levels of PM 2.5 concentrations remained low during the initial outbreak of COVID-19 pandemic in Macao (from January to February 2020), slowly recovering to pre-COVID-19 values in March 2020. As shown in Figure 4 , the levels of O 3 MAX concentration remained high during the initial outbreak of COVID-19 pandemic in Macao (from January to February 2020) and the high levels continued into March 2020. The higher levels of O 3 MAX concentration were associated with lower NO X emissions, which led to a weakened O 3 titration by NO during the COVID-19 pandemic lockdown in the nearby Guangdong Province [4] .

Despite industrial emission being a major contributor to the PM 2.5 pollution in China prior to COVID-19 pandemic lockdown period, the residential emission contributed to 39% of total PM 2.5 emissions in China, so the emissions of PM 2.5 during the lockdown period may have originated from residential areas [5] .

The comparison of PM 2.5 and O 3 MAX concentrations for Taipa Ambient during the previous year of 2019 and COVID-19 pandemic in 2020 (January to March) is presented in Figures 3 and 4 .

Despite industrial emission being a major contributor to the PM2.5 pollution in China prior to COVID-19 pandemic lockdown period, the residential emission contributed to 39% of total PM2.5 emissions in China, so the emissions of PM2.5 during the lockdown period may have originated from residential areas [5] .

The comparison of PM2.5 and O3 MAX concentrations for Taipa Ambient during the previous year of 2019 and COVID-19 pandemic in 2020 (January to March) is presented in Figures 3 and 4 . As shown in Figure 5 , the difference between monthly mean concentration (from January to March) of PM2.5 concentration in 2019 and 2020 was 16 μg/m 3 , 2 μg/m 3 , and 1 μg/m 3 , respectively. As shown in Figure 6 , the difference between monthly mean concentration (from January to March) of O3 MAX concentration in 2019 and 2020 was 12 μg/m 3 , 21 μg/m 3 , and 9 μg/m 3 , respectively.

The monthly mean concentration of PM2.5 and O3 MAX concentration for Taipa Ambient during the 

The air quality of Macao, a territory with only 32.8 km 2 , is heavily influenced by external factors, in particular by human activities that occur in the much larger and neighboring Guangdong province. Our study shows the extent to which an increase in mobility associated with Chinese National Holiday, or a decrease in the same factors, associated with the COVID-19 preventive measures period, impacts air quality in Macao.

The levels of PM2.5 concentrations significantly reduced after the first confirmed case of COVID-19 pandemic in Macao on January 22nd, 2020, which caused panic and anxiety in the local population, and continued by the announcement of casino closures by the Macao government as part of the preventive measures for COVID-19 from February 5th to 20th, 2020. As some of the preventive measures, in particular, the 15 days mandatory casino closure have been lifted, the fear and tension 

The air quality of Macao, a territory with only 32.8 km 2 , is heavily influenced by external factors, in particular by human activities that occur in the much larger and neighboring Guangdong province. Our study shows the extent to which an increase in mobility associated with Chinese National Holiday, or a decrease in the same factors, associated with the COVID-19 preventive measures period, impacts air quality in Macao.

The levels of PM 2.5 concentrations significantly reduced after the first confirmed case of COVID-19 pandemic in Macao on January 22nd, 2020, which caused panic and anxiety in the local population, and continued by the announcement of casino closures by the Macao government as part of the preventive measures for COVID-19 from February 5th to 20th, 2020. As some of the preventive measures, in particular, the 15 days mandatory casino closure have been lifted, the fear and tension of the local residents has eased, which has promoted population mobility. Although the levels of PM 2.5 concentrations in Macao improved significantly during late January and early February 2020, the levels of PM 2.5 concentrations gradually returned to normal in March 2020 after some of the preventive measures began to be lifted in Macao and its nearby Guangdong Province.

Regarding the model behavior in predicting PM 2.5 and O 3 MAX during the high pollution episode (Chinese National Holiday), observed and predicted PM 2.5 and O 3 MAX concentrations are presented in Figures 7 and 8 .

As shown in Figures 7 and 8 , the levels of PM 2.5 and O 3 MAX concentration peaked during late September and early October 2019. The PM 2.5 predicted levels followed the primary trend of the measured concentrations and followed the concentration peak represented in Figure 7 . The model for O 3 MAX also followed the primary trend, but it was more difficult to represent the concentration peak. The forecast model for PM 2.5 has a higher R 2 in comparison to the model of O 3 MAX , because the maximum hourly concentration of O 3 MAX is more challenging to predict in comparison to the 24 h average of PM 2.5, as there is influence from the regional precursors sources and also its complex chemistry with solar radiation for O 3 formation, which led to a higher degree of variability.

Due to the different nature of PM 2.5 and O 3 MAX , the forecast model performed better in the prediction of PM 2.5 in comparison to O 3 MAX. This can be demonstrated in the higher R 2 values in the PM 2.5 forecast model. The observed and predicted PM 2.5 and O 3 MAX concentrations, during the low pollution episode (implementation of COVID-19 preventive measures), are presented in Figures 9 and 10 .

The 2013 to 2018 model successfully predicted both the high and low pollution episodes, for PM 2.5 and O 3 MAX , obtaining a significant R 2 of 0.88 and 0.83, respectively, for the high pollution period (from September to November 2019), and an R 2 of 0.82 and 0.75, respectively, for the low pollution period (from January to March 2020). The R 2 obtained for the entire year of 2019 was 0.86 for both PM 2.5 and O 3 MAX . The statistical forecast model has been shown to be capable to predict, with a high coefficient of determination, the next 24 h. Int. J. Environ. Res. Public Health 2020, 17, x 14 of 19 The 2013 to 2018 model successfully predicted both the high and low pollution episodes, for PM2.5 and O3 MAX, obtaining a significant R 2 of 0.88 and 0.83, respectively, for the high pollution period (from September to November 2019), and an R 2 of 0.82 and 0.75, respectively, for the low pollution period (from January to March 2020). The R 2 obtained for the entire year of 2019 was 0.86 for both PM2.5 and O3 MAX. The statistical forecast model has been shown to be capable to predict, with a high coefficient of determination, the next 24 h.

As expected, the 2013 to 2018 model performed best with the highest R 2 

As expected, the 2013 to 2018 model performed best with the highest R 2 and lowest RMSE, MAE, and BIAS as compared with the 2013 to 2016 model and the 2015 to 2018 model. The additional two years of data helped to improve the accuracy and stability of the forecast of the 2013-2018 model.

The 2013-2018 model was able to successfully predict the high pollution episode during the Chinese National Holiday in late September and early October 2019 and the low pollution episode during the preventive measures period of COVID-19 pandemic in late January and early February 2020. This shows that this model can be reliably applied to forecast next-day pollutants concentrations across different magnitude levels of air pollution, being a useful tool for mitigation of air pollution impacts.

In addition, this shows that an improvement of global air quality in the territory is possible but it is tightly linked to the implementation of air pollution control measures in the industry and mobility sectors in Macao, in particular, in Guangdong Province. As previously studied, the air pollution problem associated with PM 2.5 and O 3 MAX is a regional problem that is not only limited to Macao, but also in the nearby regions of Hong Kong and Guangdong Province. 

As shown in Figure 5, the difference between monthly mean concentration (from January to March) of PM 2.5 concentration in 2019 and 2020 was 16 µg/m 3 , 2 µg/m 3 , and 1 µg/m 3 , respectively. As shown in Figure 6, the difference between monthly mean concentration (from January to March) of O 3 MAX concentration in 2019 and 2020 was 12 µg/m 3 , 21 µg/m 3 , and 9 µg/m 3 , respectively. and COVID-19 pandemic in 2020

As shown in Figure 6, the difference between monthly mean concentration (from January to March) of O3 MAX concentration in 2019 and 2020 was 12 μg/m 3 , 21 μg/m 3 , and 9 μg/m 3 , respectively. The monthly mean concentration of PM2.5 and O3 MAX concentration for Taipa Ambient during the previous year of 2019 and COVID-19 pandemic in 2020 (January to March) is presented in Figures 5 and 6. Overall, the preventive measures of COVID-19 pandemic may not have caused a significant difference in the levels of PM2.5 and O3 concentration in Macao

Monthly mean PM 2.5 concentrations for Taipa Ambient during the previous year of 2019 and COVID-19 pandemic in 2020

Observed and predicted PM2.5 concentrations for Taipa Ambient during Chinese National Holiday

Observed and predicted PM 2.5 concentrations for Taipa Ambient during Chinese National Holiday

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The work developed was supported by The Macao Meteorological and Geophysical Bureau (SMG).

The authors declare no conflict of interest.