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Road transportation is one of the main sources of air pollution in Macao. This study mainly explores four major roadside locations with high traffic flow in Macao from March to May 2022 and measures their pollutant concentrations (PM10 and PM2.5), traffic flow and their fuel type, as well as considering the meteorological parameters and pollutant concentration of SMG Macao to analyze the relationship between traffic flow and pollutants on roadside locations. Under the measuring distance between 3 and 6 meters, showing that the four locations had a good correlation with the roadside station data provided by SMG on both weekdays and weekends/holidays (PM2.5: R2 is 0.59 to 0.81 on weekdays and 0.79 to 0.88 on weekends/holidays, p<0.01; PM10: R2 is 0.33 to 0.82 on weekdays and 0.30 to 0.58 on weekends/holidays, p<0.05), the overall PM2.5 is 41 to 86% higher than that of the same period of Macao roadside station (SMG), and 68 to 186% higher than that of Taipa Ambient (SMG), indicating that it is more harmful to daily pedestrians. The overall relationship between PM concentration and traffic flow is small on the long-term scale (PM2.5: R2 is 0.01 to 0.13; PM10: R2 is 0.00 to 0.02). This study also analyzed air quality on EBL, the overall PM2.5 and PM10 decreased by 12.3% to 24.8% compared with non-EBL during the period, so that is indeed beneficial to the reduction of pollutant concentrations. In addition, narrower roads were overall higher when road widths added for comparison. Lastly, meteorological data added for comparison, except for relative humidity, it can be found that there is a significant correlation with long-term pollutants (p<0.05). While previous studies have found that single-day traffic flow is related to the increase in PM concentration, this paper is more inclined to their two-way effect when exploring their long-term relationship

"Over time, the large shopping malls in Macao will require some changes to improve space utilization, resulting in renovation projects that affect indoor particulate matter (PM10 and PM2.5) concentrations. Employees work long hours in an environment where the ambient air quality is poor, directly affecting their work efficiency. Nonetheless, the concentration of PM produced by the interior renovation of shopping malls has yet to receive particular attention. Therefore, this study will investigate IAQ, in particular, PM10 and PM2.5 in large-scale shopping mall renovation projects in three different indoor locations (i.e., public, renovation, and construction areas) to understand the causes of indoor PM10 and PM2.5. This study will collect on-site PM data for analysis, examine whether the current control measures are appropriate and propose some improvements. The data collected will be compared with IAQ standards from the World Health Organization (WHO) and the Macao Meteorological and Geophysical Bureau (SMG), specifically PM concentrations. The research results can provide a reference guide for decision-makers, management, construction teams, design consultant teams and renovation teams of large-scale projects. In addition, the monitoring of IAQ can ensure a comfortable environment for employees and customers."

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 iv 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 v 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 vi 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. Keywords: Air pollution; Particulate Matter; Ozone; Macao; Statistical air quality forecast; Pollution episodes; Chinese national holiday; COVID-19

" Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. While there has been continual improvement in recent years, harmful air pollutant concentration levels are still common, impacting Macau residents' health and creating long-term medical costs to local society. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration and an early alert system instead of only daily real-time reminders. Some scholars have previously carried out studies to develop an air quality forecast for Macau by successfully using statistical models. Therefore, pursuant to the outcomes of previous studies, this dissertation aims to build upon research results and explore further possibilities of building a better ML air quality forecast model based on the time series of air pollutants concentration and meteorological data. Four different state-of-the-art ML algorithms were used to create predictive models to forecast PM2.5, PM10, and carbon monoxide (CO) concentrations for the next 24 and 48-hour. These were Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). In addition, Multiple Linear Regression MLR, a standard ML model, was used for this dissertation as a baseline reference for performance comparison. The daily measurements of air quality data in Macau from 2016 to 2021 were collected for this dissertation. The 2020 and 2021 datasets were used for model testing while the four-year data prior to 2020 and 2021 were used to build and train the ML models. The results showed that SVM, ANN, RF, and XGBoost were able to provide a very good performance in building up a 24-hour forecast with higher R2 and lower RMSE, MAE, and BIAS. Meanwhile, all ML models in 48-hour forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-hour forecast. The 48-hour forecasting model could be further improved by proper feature selection based on the 24-hour dataset, using the SHAP value test, and the adjusted R2 value of the 48-hour forecasting model."

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 (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) 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 (R2), 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 PM2.5 and O3 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 PM2.5 and O3, with peaks of daily concentration exceeding 55 μg/m3 and 400 μg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and O3 levels at 50 μg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 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 levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on linear multiple regression (MR) and classification and regression trees (CART) analysis were developed successfully, for Macao, to predict the next day concentrations of NO2, PM10, PM2.5, and O3. 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. The models utilized meteorological and air quality variables based on 5 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. A wide range of meteorological and air quality variables was identified, and only some were selected as significant independent variables. Meteorological variables were selected from an extensive list of 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-h 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 be an operational air quality forecast for Macao.

Air pollution is a major concern issue on Macao since the concentration levels of several of the most common pollutants are frequently above the internationally recommended values. The low air quality episodes impacts on human health paired with highly populated urban areas are important motivations to develop forecast methodologies in order to anticipate pollution episodes, allowing establishing warnings to the local community to take precautionary measures and avoid outdoor activities during this period. Using statistical methods (multiple linear regression (MLR) and classification and regression tree (CART) analysis) we were able to develop forecasting models for the main pollutants (NO2, PM2.5, and O3) enabling us to know the next day concentrations with a good skill, translated by high coefficients of determination (0.82–0.90) on a 95% confidence level. The model development was based on six years of historical data, 2013 to 2018, consisting of surface and upper-air meteorological observations and surface air quality observations. The year of 2019 was used for model validation. From an initially large group of meteorological and air quality variables only a few were identified as significant dependent variables in the model. The selected meteorological variables included geopotential height, relative humidity and air temperature at different altitude levels and atmospheric stability characterization parameters. The air quality predictors used included recent past hourly levels of mean concentrations for NO2 and PM2.5 and maximum concentrations for O3. The application of the obtained models provides the expected daily mean concentrations for NO2 and PM2.5 and maximum hourly concentrations O3 for the next day in Taipa Ambient air quality monitoring stations. The described methodology is now operational, in Macao, since 2020.