Social restriction effects on air pollution: How the PM2.5 concentration changed with lockdown management of COVID-19 pandemic control in Bangkok Thailand

Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.

The conditional probability function (CPF) and the conditional bivariate probability function (CBPF) are widely used and useful tools that aid in identifying pollution sources in atmospheric research: they can tell us whether the wind from a particular direction increases or decreases pollutant concentrations. For this conclusion to be reliable, the observed decrease or increase must be established as statistically significant. Our literature search has shown that this is never done. Even more, we found that the majority of published analyses calculate CPF and CBPF from a relatively small number of measurements when the statistical fluctuations are large. The combination of these two facts is dangerous because it can drastically increase the likelihood of incorrect conclusion. To resolve this important issue, we have developed two independent methods for estimating the significance of the CPF and CBPF results. The methods, called binomial ratio and bootstrapping, are based on the construction of confidence intervals. We validated the methods on large and real data sets. We found them to be in a very good agreement and to have good coverage properties, which is the main criterion for the validity of confidence interval construction. The calculation and visualisation of CPF, CBPF and the associated confidence intervals was done in "CPFU", our freely available open-source software written in R. We also freely provide software that facilitates the (otherwise difficult) extraction of data files from the EPA website.

In this paper a new receptor modelling method is developed to identify and characterise emission sources. The method is an extension of the commonly used conditional probability function (CPF). The CPF approach is extended to the bivariate case to produce a conditional bivariate probability function (CBPF) plot using wind speed as a third variable plotted on the radial axis. The bivariate case provides more information on the type of sources being identified by providing important dispersion characteristic information. By considering intervals of concentration, considerably more source information can be revealed that is absent in the basic CPF or CBPF. We demonstrate the application of the approach by considering an area of high source complexity, where many new sources can be identified and characterised compared with currently used techniques. Dispersion model simulations are undertaken to verify the approach. The technique has been made available through the openair R package.

Conditional probability functions are commonly used for source identification purposes in air pollution studies. CBPF (conditional bivariate probability function) categorizes the probability of high concentrations being observed at a location by wind direction/speed and investigate the directionality of local sources. PSCF (potential source contribution function), a trajectory-ensemble method, identifies the source regions most likely to be associated with high measured concentrations. However, these techniques do not allow the direct identification of areas where changes in emissions have occurred. This study presents an extension of conditional probability methods in which the differences between conditional probability values for temporally different sets of data can be used to explore changes in emissions from source locations. The differential CBPF and differential PSCF were tested using a long-term series of air quality data (12 years; 2005/2016) collected in Rochester, NY. The probability functions were computed for each of 4 periods that represent known changes in emissions. Correlation analyses were also performed on the results to find pollutants undergoing similar changes in local and regional sources. The differential probability functions permitted the identification of major changes in local and regional emission location. In Rochester, changes in local air pollution were related to the shutdown of a large coal power plant (SO2) and to the abatement measures applied to road and off-road traffic (primary pollutants). The concurrent effects of these changes in local emissions were also linked to reduced concentrations of nucleation mode particles. Changes in regional source areas were related to the decreases in secondary inorganic aerosol and organic carbon. The differential probabilities for sulfate, nitrate, and organic aerosol were consistent with differences in the available National Emission Inventory annual emission values. Changes in the source areas of black carbon and PM2.5 mass concentrations were highly correlated.

Novel Coronavirus disease (COVID-19), after being identified in late December 2019 in Wuhan city of China, spread very fast and has affected all the countries in the world. The impact of lockdowns on particulate matter during the lockdown period needs attention to explore the correlation between anthropogenic and natural emissions. The current study has demonstrated the changes in fine particulate matter PM2.5, PM10 and their effect on air quality during the lockdown. The air quality before the lockdown was low in New Delhi (India) and Riyadh (Saudi Arabia), among major cities worldwide. The air quality of India is influenced by dust and sand from the desert and surrounding areas. Thus, the current study becomes important to analyse changes in the air quality of the Indian sub-continent as impacted by dust storms from long distances. The result indicated a significant reduction of PM2.5 and PM10 from 93.24 to 37.89 μg/m3 and from 176.55 to 98.87 μg/m3 during the lockdown period as compared to pre lockdown period, respectively. The study shows that average concentrations of PM10 and PM2.5 have declined by -44% and -59% during the lockdown period in Delhi. The average value of median PM10 was calculated at 33.71 μg/m3 for Riyadh, which was lower than that value for New Delhi during the same period. The values of PM10 were different for pre and during the lockdown periods in Riyadh, indicating the considerable influence on air quality, especially the concentration of PM10, from both the natural (sand and dust storms) and the anthropogenic sources during the lockdown periods. However, relatively smaller gains in the improvement of air quality in Riyadh were correlated to the imposition of milder lockdown and the predominance of natural factors over the anthropogenic factors there. The Air Quality Index (AQI) data for Delhi showed the air quality to be ‘satisfactory’ and in the green category during the lockdown period. This study attempts to better understand the impact of particulate matter on the short- and long-term air quality in Delhi during the lockdown. This study has the scope of being scaled up nationwide, and this might be helpful in formulation air pollution reduction and sustainable management policies in the future.

This paper reports a study on teaching and learning strategies during the COVID‐19 lockdown period (CLP) that were used by the secondary government school teachers and students in Eastern India. These teaching and learning strategies were analysed in relation to their engagement with an initiative called Integrated approach to Technology in Education (ITE). ITE engagement in the pre‐CLP involved using project‐based learning (PBL) with technology and continuous, practise‐based professional development for teachers focusing on integrating constructivist use of technology in their curriculum and pedagogy. A survey and interviews of teachers revealed that teachers with higher ITE engagement in the pre‐CLP were more likely to use PBL with technology during the CLP. Students' interviews indicated that this PBL involved deep research and technical skills that were also practised during the CLP using distance technologies. Thus, the study demonstrated adaptation of ITE innovation into a distance mode; the introduction of WebQuests during the CLP improved the likelihood of teachers engaging in project‐based teaching and PBL during the CLP. Policymakers, practitioners and researchers are recommended to adopt and continue to study the sustainability of ITE approach in new contexts such as the CLP. Practitioner notesWhat is already known about this topic Project‐based learning (PBL) with technology is related to constructivist use of technology which leads to higher order thinking skills and the learner's ability to adapt learning and knowledge in new contexts.Effective teacher professional development (TPD) is continuous, uses community of practise (CoP) groups and offers opportunities to practise new knowledge and skills in the classroom context. TPD for technology integration allows the demonstration and practise of constructivist pedagogies.Access to devices, connectivity and digital resources in the local language are major issues faced by students to connect with teaching and learning in the COVID‐lockdown period (CLP), especially students in underprivileged settings. What this paper adds Prior exposure to PBL with technology and effective TPD when combined with demonstrations of this learning in a new context (COVID‐19 lockdown period, CLP required distance mode) will increase the likelihood of implementing PBL with technology in this new context.Besides increased use of technology, the CLP created a scope for adoption of teaching and learning pedagogies in the post‐CLP, such as the use of Instant Messaging Application (IMA) for a flipped classroom experience across home/social and school settings. Implications for practice and/or policy Standardised digital resources may not be the only solution for teaching and learning when schools have to be closed.Constructivist use of technology by students with project‐based learning (PBL), artefact creation on authentic tasks and inquiry‐based learning has the potential to transform students from passive consumers into active adapters in newer contexts like COVID‐19 lockdown period (CLP).Investment in practise‐based continuous professional development of teachers can develop a sense of agency and competence so that teachers can adapt technologies for constructive teaching pedagogies and mitigate access related challenges for students in different contexts.Integrate new teaching and learning strategies that emerged during the CLP in the curriculum and pedagogy of both in‐service and pre‐service teacher professional development.

Abstract. Total gaseous mercury (TGM) concentrations were measured every 5 min in Pohang, Gyeongsangbuk-do, Korea, during summer (17–23 August 2012), fall (9–17 October 2012), winter (22–29 January 2013), and spring (26 March–3 April 2013) to (1) characterize the hourly and seasonal variations of atmospheric TGM concentrations; (2) identify the relationships between TGM and co-pollutants; and (3) identify likely source directions and locations of TGM using the conditional probability function (CPF), conditional bivariate probability function (CBPF) and total potential source contribution function (TPSCF). The TGM concentration was statistically significantly highest in fall (6.7 ± 6.4 ng m−3), followed by spring (4.8 ± 4.0 ng m−3), winter (4.5 ± 3.2 ng m−3) and summer (3.8 ± 3.9 ng m−3). There was a weak but statistically significant negative correlation between the TGM concentration and ambient air temperature (r = −0.08, p<0.05). Although the daytime temperature (14.7 ± 10.0 °C) was statistically significantly higher than that in the nighttime (13.0 ± 9.8 °C) (p<0.05), the daytime TGM concentration (5.3 ± 4.7 ng m−3) was statistically significantly higher than that in the nighttime (4.7 ± 4.7 ng m−3) (p<0.01), possibly due to local emissions related to industrial activities and activation of local surface emission sources. The observed ΔTGM ∕ ΔCO was significantly lower than that of Asian long-range transport, but similar to that of local sources in Korea and in US industrial events, suggesting that local sources are more important than those of long-range transport. CPF, CBPF and TPSCF indicated that the main sources of TGM were iron and manufacturing facilities, the hazardous waste incinerators and the coastal areas.

High ozone levels were often observed from December to April at suburban stations in the Bangkok Metropolitan Region (BMR). Wind direction analyses show significant roles of air masses from outside BMR on elevating the BMR O 3 levels. These air masses include biomass burning smoke from the northwestern BMR and sea spray from the Gulf of Thailand mixing with industrial emissions from the southern BMR. Seasonal O 3 profiles corresponded with NO 2 and CO trends and diurnal O 3 corresponded with solar radiation. Diurnal peaks of NO 2 and CO were results of traffic emissions during traffic rush hours, whereas nighttime NO 2 peaks, for northern BMR, attributed to NO-O 3 reactions. Both NO 2 and CO at 7:00–9:00 highly correlated with the O 3 in the following afternoon. Principal component analysis (PCA) and an analysis of non-methane hydrocarbons-to-NO 2 ratio suggest the NO 2 -limited O 3 formation in the BMR suburb. The PCA also suggests a negative response of wind speeds on the afternoon O 3 formation, whereas roles of ambient temperature on the O 3 were seasonally different.

COVID-19-pandemic-related home confinement aids in limiting the spread of the virus but restricts exposure to sunlight, thereby possibly affecting 25(OH)D concentrations. This study aimed to investigate the effect of lockdown measures on 25(OH)D levels in outpatients visiting the healthcare centre over a period of two years. In this retrospective chart review, outpatients who visited a university healthcare centre for a health check-up over a period of two years were included. The patients' 25(OH)D serum levels and status were compared before, during, and after the lockdown periods. A total of 7234 patients were included in this study, with a mean age of 34.66 ± 16.78. The overall prevalence of 25(OH)D insufficiency, deficiency and sufficiency was 33.8%, 30.7% and 35.4%, respectively. The proportion of individuals with 25-(OH) D deficiency prior to lockdown was 29% and this proportion increased in the lockdown and post-lockdown periods to 31.1% and 32%, respectively. Although gender was less likely to have an impact on the 25 (OH) D level during the lockdown period (p = 0.630), we found an association between gender and 25 (OH) D status in the pre-lockdown and post-lockdown periods (p < 0.001 and p < 0.001, respectively). Another association between nationality and 25 (OH)D levels was found before, during and after the lockdown periods (p < 0.001). In addition, the youngest population, aged between 1 and 14, was strongly affected by the home confinement. Age had a positive and significant (p < 0.05) effect on 25 (OH) D status regardless of the different periods. Moreover, in the pre-lockdown period, male outpatients had 1.56 chance of having a sufficient level of 25 (OH)D. However, during the lockdown period, this chance decreased to 0.85 and then increased to 0.99 after the lockdown period. We found no statistically significant difference in the mean serum concentrations or in the prevalence of vitamin D insufficiency when we compared values from before, during and immediately after the COVID-19 lockdown period. However, there was a generally increased prevalence of vitamin D insufficiency in our study population. Another association between gender, nationality and age groups with 25(OH) D was found. Regular exposure to UVR is recommended for maintaining adequate vitamin D levels and to prevent vitamin D deficiency. Further research is needed to determine the best indications for vitamin D supplementation if confinement periods are extended and to consider the potential health consequences of prolonged confinement periods not only on vitamin D status but also on overall public health. The findings of this study may be considered by stakeholders for a targeted supplementation approach for risk groups.

&#x0D; Introduction: Commuters spend countless hours within tightly confined spaces with limited ventilation that may be filled with many contaminants. By analyzing if there is a significant difference between levels of carbon dioxide between rush and non-rush hour conditions, it can be determined if some commuters are subjected to poorer levels of air quality during certain times of the day. Objectives: The primary objective of this study is to understand whether there are significant ventilation deficiencies during rush compared to non-rush hour times in urban transport microenvironments. Methods: Analysis of urban transport microenvironments was done using the TSI brand QTrak Indoor Air Quality (IAQ) Monitor to gather data on carbon dioxide at 1-minute intervals on the 99 B-line express bus that runs between Broadway and Commercial Skytrain Station and the Broadway and Cambie Street Skytrain Station. Results: A one tailed T-test was done on the NCSS 9 statistical software to compare if rush hour urban microenvironments had significantly higher concentrations of carbon dioxide than when compared to non-rush hour. Statistical analysis determined that since the P-value was well above the alpha level of 0.05 (i.e. P&lt;0.05), it gives reason to accept the null hypothesis, which states that rush hour concentrations were not higher than non-rush hour. Conclusion: Statistical analysis determined that the overall concentrations of carbon dioxide during rush hour were not significantly higher than non-rush hour times. This result may have been attributed to conditions and factors during data collection that could not be controlled by the researcher. Due to the length of the route, exposure times were found to be within time-weighted averages as set out by the American Conference of Industrial Hygienists (ACGIH), though it was still not within the recommended limit of 1000 ppm as set out by the American Society of Heating, Refrigeration and Air-Conditioning (ASHRAE).&#x0D;

This study was conducted to monitor the impact of human activities on the air quality of Port Harcourt Metropolis, during and after the Covid-19 Lockdown period where human activities were brought to an all-time low. Ten selected locations, evenly distributed around Port Harcourt Metropolis were studied while Rivers State University served as the Control Site. The meteorological parameters studied alongside carbon monoxide were atmospheric temperature, relative humidity, wind speed, and wind direction. Standard methods were applied in capturing data in-situ, using hand-held devices. Data were collected for six months capturing rainy and dry season months and the lockdown periods. Air Quality Indices were also calculated for CO. The results obtained reveal that meteorological data gathered were characteristic of the study area. The values of carbon monoxide were higher in the Sampling Sites with a mean range of (0.632 – 3.291) ppm in the rainy season and (0.422 – 9.037) ppm in the dry season) than in the Control Site with a mean of (0.133 ppm in the rainy season and 0.493 ppm in the dry season). The level of CO was higher in the dry season as compared to the rainy season. There was strong positive correlation with respect to carbon monoxide amongst sampling locations with similar human activities. The correlation of the sampling locations with the Control Site were mostly weak. A reduction of the concentrations of carbon monoxide was recorded during the Covid-19 Lockdown periods. The AQI values indicate that the air quality was categorized as good with respect to carbon monoxide in the lockdown periods and was between moderate to unhealthy in the non-lockdown periods. It is clearly evident that air quality is a function of the level of human activities, especially those that result in the emission of noxious air pollutants. It is recommended that the air quality be put under regular monitoring in order to regulate the human activities that can impact on the air quality.

Impact on air pollution in Delhi and incidence of stroke in lieu of COVID-19 lockdown

PM2.5 carbonaceous components and mineral dust at a COALESCE network site - Bhopal, India: Assessing the impacts of COVID-19 lockdowns on site-specific optical properties

BackgroundSeveral studies have shown the health effects of air pollutants, especially in China, North American and Western European countries. But longitudinal cohort studies focused on health effects of long-term air pollution exposure are still limited in Southeast Asian countries where sources of air pollution, weather conditions, and demographic characteristics are different. The present study examined the association between long-term exposure to air pollution and self-reported morbidities in participants of the Thai cohort study (TCS) in Bangkok metropolitan region (BMR), Thailand. MethodsThis longitudinal cohort study was conducted for 9 years from 2005 to 2013. Self-reported morbidities in this study included high blood pressure, high blood cholesterol, and diabetes. Air pollution data were obtained from the Thai government Pollution Control Department (PCD). Particles with diameters ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) exposures were estimated with ordinary kriging method using 22 background and 7 traffic monitoring stations in BMR during 2005–2013. Long-term exposure periods to air pollution for each subject was averaged as the same period of person-time. Cox proportional hazards models were used to examine the association between long-term air pollution exposure with self-reported high blood pressure, high blood cholesterol, diabetes. Results of self-reported morbidity were presented as hazard ratios (HRs) per interquartile range (IQR) increase in PM10, O3, NO2, SO2, and CO. ResultsAfter controlling for potential confounders, we found that an IQR increase in PM10 was significantly associated with self-reported high blood pressure (HR = 1.13, 95% CI: 1.04, 1.23) and high blood cholesterol (HR = 1.07, 95%CI: 1.02, 1.12), but not with diabetes (HR = 1.05, 95%CI: 0.91, 1.21). SO2 was also positively associated with self-reported high blood pressure (HR = 1.22, 95%CI: 1.08, 1.38), high blood cholesterol (HR = 1.20, 95%CI: 1.11, 1.30), and diabetes (HR = 1.21, 95%CI: 0.92, 1.60). Moreover, we observed a positive association between CO and self-reported high blood pressure (HR = 1.07, 95%CI: 1.00, 1.15), but not for other diseases. However, self-reported morbidities were not associated with O3 and NO2. ConclusionsLong-term exposure to air pollution, especially for PM10 and SO2 was associated with self-reported high blood pressure, high blood cholesterol, and diabetes in subjects of TCS. Our study supports that exposure to air pollution increases cardiovascular disease risk factors for younger population.

The COVID-19 lockdown has not only helped in combating the community transmission of SARS-CoV-2 but also improved air quality in a very emphatic manner in most of the countries. In India, the first phase of COVID-19 lockdown came into force on March 25, 2020, which was later continued in the next phases. The purpose of this study was to investigate the result of lockdown on air quality of major metropolitan cities—Delhi, Mumbai, Kolkata, Chennai, Bengaluru, Hyderabad, Jaipur, and Lucknow—from March 25 to May 3, 2020. For this study, the concentration of six criteria air pollutants (PM2.5, PM10, CO, NO2, SO2, and O3) and air quality index during the COVID-19 lockdown period was compared with the same period of the previous year 2019. The results indicate a substantial improvement in air quality with a drastic decrease in the concentration of PM2.5, PM10, CO, and NO2, while there is a moderate reduction in SO2 and O3 concentration. During the lockdown period, the maximum reduction in the concentration of PM2.5, PM10, CO, NO2, SO2, and O3 was observed to be − 49% (Lucknow), − 57% (Delhi), − 75% (Mumbai), − 68% (Kolkata), − 48% (Mumbai), and − 29% (Hyderabad), respectively. The value of the air quality index (AQI) also dwindled significantly during the COVID-19 lockdown period. The maximum decline in AQI was observed – 52% in Bengaluru and Lucknow. The order of AQI was satisfactory > moderate > good > poor and the frequency order of prominent pollutants was O3 > PM10 > PM2.5 > CO > NO2 > SO2 during the lockdown period in all the aforementioned metropolitan cities.Graphic abstract