The Impact of COVID-19 on NO2 and PM2.5 Levels and Their Associations with Human Mobility Patterns in Singapore

The decline in NO2 and PM2.5 pollutant levels were observed during COVID-19 around the world, especially during lockdowns. Previous studies explained such observed decline with the decrease in human mobility, whilst overlooking the meteorological changes (e.g., rainfall, wind speed) that could mediate air pollution level simultaneously. This pitfall could potentially lead to over-or under-estimation of the effect of COVID-19 on air pollution. Consequently, this study aims to re-evaluate the impact of COVID-19 on NO2 and PM2.5 pollutant level in Singapore, by incorporating the effect of meteorological parameters in predicting NO2 and PM2.5 baseline in 2020 using machine learning methods. The results found that NO2 and PM2.5 declined by a maximum of 38% and 36%, respectively, during lockdown period. As two proxies for change in human mobility, taxi availability and carpark availability were found to increase and decrease by a maximum of 12.6% and 9.8%, respectively, in 2020 from 2019 during lockdown. To investigate how human mobility influenced air pollutant level, two correlation analyses were conducted: one between PM2.5 and carpark availability changes at regional scale and the other between NO2 and taxi availability changes at a spatial resolution of 0.01°. The NO2 variation was found to be more associated with the change in human mobility, with the correlation coefficients vary spatially across Singapore. A cluster of stronger correlations were found in the South and East Coast of Singapore. Contrarily, PM2.5 and carpark availability had a weak correlation, which could be due to the limit of regional analyses. Drawing to the wider context, the high association between human mobility and NO2 in the South and East Coast area can provide insights into future NO2 reduction policy in Singapore.Graphical Abstract

MEASUREMENT AND EVALUATION OF AIR QUALITY MONITORING TOOL FOR PASSENGER VEHICLES

One of the major health environmental issues is air quality. Polluted air harms human health and contributes to environmental changes such as global warming and weather fluctuations. Outdoor air quality pollution has been one of the world's biggest problems for decades. This problem is closely related to the vehicle, which is the most significant source of pollutants. Nowadays, there is a lack of technology available to the public to provide a monitoring tool for users in the vehicle context. People cannot trace or monitor the air quality their vehicle emits and the air quality within the vehicle itself. Existing projects have been studied, and improvements have been made to ensure that the study is aligned and attainable within the scope. This study developed an air quality monitoring tool based on six types of pollutants, i.e., carbon monoxide, particle matters, ground-level ozone, nitrogen dioxide, and sulfur dioxide, which can be used in a vehicle context. The tool's main purpose is to provide vehicle users with real-time readings of the amount of pollutants from their vehicle and transmit the data to a dashboard for visualization. If the pollutant level exceeds the safe limit, a notification will be sent to the user. Experiments revealed that the air quality in the vehicle might be attributed to the vehicle's current state (idle or moving), duration of the running engine, prolonged exposure to the weather, and outdoor air quality.

A New Approach for Reducing Pollutants Level: A Longitudinal Cohort Study of Physical Exercises in Young People

Background: The present study aimed to evaluate the elimination of three common pollutants (dimethoate, benzo(a)pyrene (BaP) and bisphenol A (BPA) by different physical exercises and to assess the possible factors which could affect the pollutants elimination. Methods: A total of 200 individuals who chose different kinds of exercises in accordance to their own wish were recruited. The levels of urinary pollutants were measured using β-glucuronidase hydrolysis followed by a high-performance liquid chromatography tandem mass spectrometry-based method.Results: Totally, the levels of dimethoate, BaP and bisphenol A were reduced after physical exercises. However, the elimination of BaP in male was higher than that in female but the elimination of bisphenol A in female was higher than that in male. Multivariate logistic regression showed that the degree of HR change was a protective factor affecting the improvement effect of dimethoate, BaP and bisphenol A while BMI (body mass index) was a risk factor. Nevertheless, sex was a risk factor affecting the improvement of dimethoate and BaP but had a lower efficacy on bisphenol A improvement.Conclusion: The present findings indicate that physical exercises can be considered as a novel approach to eliminate pollutants level in human body and can also give suggestions for choosing specific physical exercises to male and female individuals. Moreover, those who are with higher BMI need to lose weight before eliminating pollutant level through physical exercises.

Indoor Emission Sources Detection by Pollutants Interaction Analysis

This study employs the correlation coefficients technique to support emission sources detection for indoor environments. Unlike existing methods analyzing merely primary pollution, we consider alternatively the secondary pollution (i.e., chemical reactions between pollutants in addition to pollutant level), and calculate intra pollutants correlation coefficients for characterizing and distinguishing emission events. Extensive experiments show that seven major indoor emission sources are identified by the proposed method, including (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session. Furthermore, our method improves the detection accuracy by a support vector machine compared to without data filtering and applying typical feature extraction methods such as PCA and LDA.

Sebaran Cemaran Hg di Kawasan Pertambangan Emas Rakyat Menggunakan Metode Inverse Distance Weight

Gold mining on mining communities supports economic life for the societies. On the other hands, these activities are not only support the economic but also give bad effects to the environment. Some people are still using amalgamation process to gold ore process which has the potential to spread Hg concentration in the main area. Spatial distribution of Hg concentration was found in this study. The purpose of this research was identifying the contamination of Hg concetration in three villages of society’s mining gold areas in Kertajaya Sukabumi, West Java. Those three villages were Cigadog Village, Pondok Tilu Citamiang Village, and Kiara 2 Village. Hg concentration distribution map was obtained several samples which were taking 6 soil samples in Cigadog village, 5 soil samples from Pondok Tilu Citamiang Village, and 4 soil samples in Kiara 2 Village. Those samples were analysed for Hg concentration using AAS (Atomic Absorption Spectrophotometer) method. Inverse Distance Weight method was used as Data processing and it was helped by ArcGIS software. The result showed that soil samples from Cigadong village contained Hg concentration of 0.28 - 2.84 ppm, 83% samples were critically polluted, and the contaminated areas were 5.888 hectares. Whilst, soil samples from Pondok Tilu Citamiang Village contained Hg concentration of 0.14 - 1.26 ppm, 80% samples were critically polluted, and the polluted areas were 1.476 Ha. Then, soil samples from Kiara 2 Village contained Hg concentration of 0.67-6.19 ppm, 100% samples were critically polluted, and the contaminated area was 0.040 hectare. The findings of the contaminated area and the pollutant level in mining societies in Kertajaya Village, Sukabumi could be used as initial input for the efforts to restore the Hg polluted environment.

Rigorous Quantification of the statistical significance of COVID-19 lockdown effect on air quality: The case from ground-based measurements in Ontario, Canada

Preliminary analysis of satellite measurements from around the world showed drops in nitrogen dioxide (NO₂) with lockdowns due to the COVID-19 pandemic. A number of studies have found these drops to be correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical testing framework that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO₂ (11 out 16 sites) and CO (all 4 sites) in April-June 2020, with pollutant levels 20% lower than in the previous three years. Much fewer sites (2-3 out of 16) experienced statistically significant drops in O₃ and PM2.5.<b> </b>The statistical testing framework developed here is the first of its kind applied to air quality data, and highlights the need for rigorous assessment of statistical significance, should analyses of pollutant level changes post COVID-19 lockdowns be used to inform policy decisions in Ontario, Canada. See Methods section in the manuscript.

Rigorous Quantification of the statistical significance of COVID-19 lockdown effect on air quality: The case from ground-based measurements in Ontario, Canada

Preliminary analysis of satellite measurements from around the world showed drops in nitrogen dioxide (NO₂) with lockdowns due to the COVID-19 pandemic. A number of studies have found these drops to be correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical testing framework that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO₂ (11 out 16 sites) and CO (all 4 sites) in April-June 2020, with pollutant levels 20% lower than in the previous three years. Much fewer sites (2-3 out of 16) experienced statistically significant drops in O₃ and PM2.5.<b> </b>The statistical testing framework developed here is the first of its kind applied to air quality data, and highlights the need for rigorous assessment of statistical significance, should analyses of pollutant level changes post COVID-19 lockdowns be used to inform policy decisions in Ontario, Canada. See Methods section in the manuscript.

Statistical Quantification of COVID-19 Lockdown Effect on Air Quality from Ground-Based Measurements in Ontario, Canada

Preliminary analysis of satellite measurements from around the world showed drops in nitrogen dioxide (NO₂) with lockdowns due to the COVID-19 pandemic. A number of studies have found these drops to be correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical testing framework that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO₂ (11 out 16 sites) and CO (all 4 sites) in April-June 2020, with pollutant levels 20% lower than in the previous three years. Much fewer sites (2-3 out of 16) experienced statistically significant drops in O₃ and PM2.5.<b> </b>The statistical testing framework developed here is the first of its kind applied to air quality data, and highlights the need for rigorous assessment of statistical significance, should analyses of pollutant level changes post COVID-19 lockdowns be used to inform policy decisions in Ontario, Canada. See Methods section in the manuscript.