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1.
Int J Environ Res Public Health ; 19(10)2022 05 18.
Article in English | MEDLINE | ID: covidwho-1862786

ABSTRACT

Since the COVID-19 epidemic outbreak at the end of 2019, many studies regarding the impact of meteorological factors on the attack have been carried out, and inconsistent conclusions have been reached, indicating the issue's complexity. To more accurately identify the effects and patterns of meteorological factors on the epidemic, we used a combination of logistic regression (LgR) and partial least squares regression (PLSR) modeling to investigate the possible effects of common meteorological factors, including air temperature, relative humidity, wind speed, and surface pressure, on the transmission of the COVID-19 epidemic. Our analysis shows that: (1) Different countries and regions show spatial heterogeneity in the number of diagnosed patients of the epidemic, but this can be roughly classified into three types: "continuous growth", "staged shock", and "finished"; (2) Air temperature is the most significant meteorological factor influencing the transmission of the COVID-19 epidemic. Except for a few areas, regional air temperature changes and the transmission of the epidemic show a significant positive correlation, i.e., an increase in air temperature is conducive to the spread of the epidemic; (3) In different countries and regions studied, wind speed, relative humidity, and surface pressure show inconsistent correlation (and significance) with the number of diagnosed cases but show some regularity.


Subject(s)
COVID-19 , Epidemics , COVID-19/epidemiology , Humans , Meteorological Concepts , Meteorology , Wind
2.
Environ Res ; 212(Pt B): 113214, 2022 09.
Article in English | MEDLINE | ID: covidwho-1778116

ABSTRACT

Existing studies reported higher altitudes reduce the COVID-19 infection rate in the United States, Colombia, and Peru. However, the underlying reasons for this phenomenon remain unclear. In this study, regression analysis and mediating effect model were used in a combination to explore the altitudes relation with the pattern of transmission under their correlation factors. The preliminary linear regression analysis indicated a negative correlation between altitudes and COVID-19 infection in China. In contrast to environmental factors from low-altitude regions (<1500 m), high-altitude regions (>1500 m) exhibited lower PM2.5, average temperature (AT), and mobility, accompanied by high SO2 and absolute humidity (AH). Non-linear regression analysis further revealed that COVID-19 confirmed cases had a positive correlation with mobility, AH, and AT, whereas negatively correlated with SO2, CO, and DTR. Subsequent mediating effect model with altitude-correlated factors, such as mobility, AT, AH, DTR and SO2, suffice to discriminate the COVID-19 infection rate between low- and high-altitude regions. The mentioned evidence advance our understanding of the altitude-mediated COVID-19 transmission mechanism.


Subject(s)
COVID-19 , Altitude , COVID-19/epidemiology , China/epidemiology , Colombia , Humans , Meteorological Concepts , Meteorology
3.
Environ Monit Assess ; 194(4): 274, 2022 Mar 14.
Article in English | MEDLINE | ID: covidwho-1739369

ABSTRACT

Most of the published articles which document changes in atmospheric compositions during the various lockdown and unlock phases of COVID-19 pandemic have made a direct comparison to a reference point (which may be 1 year apart) for attribution of the COVID-mediated lockdown impact on atmospheric composition. In the present study, we offer a better attribution of the lockdown impacts by also considering the effect of meteorology and seasonality. We decrease the temporal distance between the impacted and reference points by considering the difference of adjacent periods first and then comparing the impacted point to the mean of several reference points in the previous years. Additionally, we conduct a multi-station analysis to get a holistic effect of the different climatic and emission regimes. In several places in eastern and coastal India, the seasonally induced changes already pointed to a decrease in PM concentrations based on the previous year data; hence, the actual decrease due to lockdown would be much less than that observed just on the basis of difference of concentrations between subsequent periods. In contrast, northern Indian stations would normally show an increase in PM concentration at the time of the year when lockdown was effected; hence, actual lockdown-induced change would be in surplus of the observed change. The impact of wind-borne transport of pollutants to the study sites dominates over the dilution effects. Box model simulations point to a VOC-sensitive composition.


Subject(s)
Air Pollutants , COVID-19 , Air Pollutants/analysis , COVID-19/epidemiology , Communicable Disease Control , Environmental Monitoring , Humans , Meteorology , Pandemics
4.
Environ Pollut ; 300: 118932, 2022 May 01.
Article in English | MEDLINE | ID: covidwho-1664904

ABSTRACT

Air pollution is becoming serious in developing country, and how to quantify the role of local emission and/or meteorological factors is very important for government to implement policy to control pollution. Here, we use a random forest model, a machine learning (ML) approach, combined with a de-weather method to analyze the PM2.5 level during the COVID-19 outbreak in Hubei Province. The results show that changes in anthropogenic emissions have reduced PM2.5 concentrations in February and March 2020 by about 33.3% compared to the same period in 2019, while changes in meteorological conditions have increased PM2.5 concentrations by about 8.8%. Moreover, the impact of meteorological conditions is more significant in the central region, which is likely to be related to regional transport. After excluding the contribution of meteorological conditions, the PM2.5 concentration in Hubei Province in February and March 2020 is lower than the secondary standard of China (35 µ g/m3). Our estimates also indicate that under similar meteorological conditions as in February and March 2019, an emission reduction intensity equivalent to about 48% of the emission reduction intensity during the lockdown may bring the annual average PM2.5 concentration to the standard (35 µ g/m3). Our study shows that machine learning is a powerful tool to quantify the influencing factors of PM2.5, and the results further emphasize the need for scientific emission reduction as well as joint regional control measures in future.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , COVID-19/epidemiology , China/epidemiology , Communicable Disease Control , Disease Outbreaks , Environmental Monitoring , Humans , Machine Learning , Meteorology , Particulate Matter/analysis , SARS-CoV-2
5.
Int J Environ Res Public Health ; 18(24)2021 12 18.
Article in English | MEDLINE | ID: covidwho-1580723

ABSTRACT

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the "deweather" R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO2, PM10 and O3 data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013-2019). Data from a northern Spanish region, Cantabria, with all types of monitoring stations (traffic, urban background, industrial and rural) were used, dividing the calendar year into eight periods according to the intensity of government restrictions. The results showed mean reductions in the lockdown period above -50% for NOx, around -10% for PM10 and below -5% for O3. Small differences were found between the relative changes obtained from normalised data with respect to those from observations. These results highlight the importance of developing an integrated policy to reduce anthropogenic emissions and the need to move towards sustainable mobility to ensure safer air quality levels, as pre-existing concentrations in some cases exceed the safe threshold.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Communicable Disease Control , Environmental Monitoring , Humans , Meteorology , Pandemics , Particulate Matter/analysis , SARS-CoV-2
6.
Environ Sci Process Impacts ; 23(11): 1718-1728, 2021 Nov 17.
Article in English | MEDLINE | ID: covidwho-1500758

ABSTRACT

Indian cities can experience severe air pollution, and the reduction in activity during the first national COVID-19 lockdown (2020) offered a natural experiment to study the contribution of local sources. The current work aimed to quantify the changes due to the lockdown in NOx, O3 and PM2.5 in two contrasting cities in India (Delhi and Hyderabad) using a boosted regression tree model to account for the influence of meteorology. The median NOx and PM2.5 concentrations were observed to decrease after lockdown in both cities, up to 57% and 75% for PM2.5 and NOx, respectively when compared to previous years. After normalization due to meteorology the calculated reduction after lockdown for PM2.5 was small (<8%) in both cities, and was likely less attributable to changes in local emissions, but rather due changes in background levels (i.e. regional source(s)). The reduction of NOx due to lockdown varied by site (on average 5-30%), likely reflecting differences in relative proximity of local sources to the monitoring site, demonstrating the key influence of meteorology on ambient levels post-lockdown. Ozone was observed to increase after lockdown at both sites in Delhi, likely due to changes in relative amounts of precursor concentrations promoting ozone production, suggesting a volatile organic compound (VOC)-limited regime in Delhi. Thus, the calculated reduction in air pollutants due to lockdown in the current work cannot be extrapolated to be solely from a reduction in emissions and instead reflects the overall change in ambient levels, as meteorology and atmospheric chemical processes also contributed.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Cities , Communicable Disease Control , Environmental Monitoring , Humans , Meteorology , Particulate Matter/analysis , SARS-CoV-2
7.
Environ Monit Assess ; 193(9): 618, 2021 Sep 02.
Article in English | MEDLINE | ID: covidwho-1391917

ABSTRACT

Recent studies concluded that air quality has improved due to the enforcement of lockdown in the wake of COVID-19. However, they mostly concentrated on the changes during the lockdown period, and the studies considering the consequences of de-escalation of lockdown are inadequate. Therefore, we investigated the changes in fine particulate matter (PM2.5) during the pre-lockdown, strict lockdown, unlocking, and post-lockdown scenarios. In addition, we assessed the influence of meteorology, mobility, air mass transport, and biomass burning on PM2.5 using Google's mobility data, back trajectory model, and satellite-based fire incident data. Average PM2.5 concentrations in Ghaziabad, Noida, and Faridabad decreased by 60.70%, 63.27%, and 60.40%, respectively, during the lockdown. When compared with the preceding year (2019), the reductions during the shutdown period (25 March-31 May) were within the range of 36.34-44.55%. However, considering the entire year, this reduction in PM2.5 is momentary, and a steady increase in traffic density and industrial operations within cities during post-lockdown reflects a potent recovery of aerosol level, during which the average mass of PM2.5 three- to four-folds higher than the lockdown period. Back trajectories and fire activity results showed that biomass burning in the nearby states (Haryana and Punjab) influence aerosol load. We conclude that a partial lockdown in the event of a sudden surge in pollution would be a beneficial approach. However, reducing fossil fuel consumption and switching to more environmentally friendly energy sources, developing green transport networks, and circumventing biomass burning are efficient ways to improve air quality in the long term.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Biomass , Cities , Communicable Disease Control , Environmental Monitoring , Humans , Meteorology , Particulate Matter/analysis , SARS-CoV-2
8.
Sci Total Environ ; 803: 149931, 2022 Jan 10.
Article in English | MEDLINE | ID: covidwho-1373255

ABSTRACT

Economic and urban development in sub-Saharan Africa (SSA) may be shifting the dominant air pollution sources in cities from biomass to road traffic. Considered as a marker for traffic-related air pollution in cities, we conducted a city-wide measurement of NOx levels in the Accra Metropolis and examined their spatiotemporal patterns in relation to land use and meteorological factors. Between April 2019 to June 2020, we collected weekly integrated NOx (n = 428) and NO2 (n = 472) samples at 10 fixed (year-long) and 124 rotating (week-long) sites. Data from the same time of year were compared to a previous study (2006) to assess changes in NO2 concentrations. NO and NO2 concentrations were highest in commercial/business/industrial (66 and 76 µg/m3, respectively) and high-density residential areas (47 and 59 µg/m3, respectively), compared with peri-urban locations. We observed annual means of 68 and 70 µg/m3 for NO and NO2, and a clear seasonal variation, with the mean NO2 of 63 µg/m3 (non-Harmattan) increased by 25-56% to 87 µg/m3 (Harmattan) across different site types. The NO2/NOx ratio was also elevated by 19-28%. Both NO and NO2 levels were associated with indicators of road traffic emissions (e.g. distance to major roads), but not with community biomass use (e.g. wood and charcoal). We found strong correlations between both NO2 and NO2/NOx and mixing layer depth, incident solar radiation and water vapor mixing ratio. These findings represent an increase of 25-180% when compared to a small study conducted in two high-density residential neighborhoods in Accra in 2006. Road traffic may be replacing community biomass use (major source of fine particulate matter) as the prominent source of air pollution in Accra, with policy implication for growing cities in SSA.


Subject(s)
Air Pollutants , Air Pollution , Air Pollutants/analysis , Air Pollution/analysis , Environmental Monitoring , Meteorology , Nitrogen Dioxide/analysis , Nitrogen Oxides/analysis , Particulate Matter/analysis
9.
Huan Jing Ke Xue ; 42(7): 3099-3106, 2021 Jul 08.
Article in Chinese | MEDLINE | ID: covidwho-1332912

ABSTRACT

This study analyzed the impacts of meteorological conditions and changes in air pollutant emissions on PM2.5 across the country during the first quarter of 2020 based on the WRF-CMAQ model. Results showed that the variations in meteorological conditions led to a national PM2.5 concentration decreased of 1.7% from 2020-01 to 2020-03, whereas it increased by 1.6% in January and decreased by 1.3% and 7.9% in February and March, respectively. The reduction of pollutants emissions led to a decrease of 14.1% in national PM2.5 concentration during the first quarter of 2020 and a decrease of 4.0%, 25.7%, and 15.0% in January, February, and March, respectively. Compared to the same period last year, the PM2.5 concentration measured in Wuhan City decreased more than in the entire country. This was caused by improved meteorological conditions and a higher reduction of pollutant emissions in Wuhan City. PM2.5 in Beijing increased annually before the epidemic outbreak and during the strict control period, mainly due to unfavorable meteorological conditions. However, the decrease in PM2.5 in Beijing compared to March 2019 was closely related to the substantial reduction of emissions. The measured PM2.5 in the "2+26" cities, the Fenwei Plain and the Yangtze River Delta (YRD) decreased during the first quarter of 2020, with the largest drop occurring in the Yangtze River Delta due to higher YRD emissions reductions. The meteorological conditions of "2+26" cities and Fenwei Plain were unfavorable before the epidemic outbreak and greatly improved during the strict control period, whereas the Yangtze River Delta had the most favorable meteorological conditions in March. The decrease in PM2.5 concentration caused by the reduction of pollutant emissions in the three key areas was highest during the strict control period.


Subject(s)
Air Pollutants , Air Pollution , Epidemics , Air Pollutants/analysis , Air Pollution/analysis , Beijing , China , Cities , Environmental Monitoring , Meteorology , Particulate Matter/analysis
10.
Sci Rep ; 11(1): 11119, 2021 05 27.
Article in English | MEDLINE | ID: covidwho-1328852

ABSTRACT

To analyse the cause of the atmospheric PM2.5 pollution that occurred during the COVID-19 lockdown in Nanning, Guangxi, China, a single particulate aerosol mass spectrometer, aethalometer, and particulate Lidar coupled with monitoring near-surface gaseous pollutants, meteorological conditions, remote fire spot sensing by satellite and backward trajectory models were utilized during 18-24 February 2020. Three haze stages were identified: the pre-pollution period (PPP), pollution accumulation period (PAP) and pollution dissipation period (PDP). The dominant source of PM2.5 in the PPP was biomass burning (BB) (40.4%), followed by secondary inorganic sources (28.1%) and motor vehicle exhaust (11.7%). The PAP was characterized by a large abundance of secondary inorganic sources, which contributed 56.1% of the total PM2.5 concentration, followed by BB (17.4%). The absorption Ångström exponent (2.2) in the PPP was higher than that in the other two periods. Analysis of fire spots monitored by remote satellite sensing indicated that open BB in regions around Nanning City could be one of the main factors. A planetary boundary layer-relative humidity-secondary particle matter-particulate matter positive feedback mechanism was employed to elucidate the atmospheric processes in this study. This study highlights the importance of understanding the role of BB, secondary inorganic sources and meteorology in air pollution formation and calls for policies for emission control strategies.


Subject(s)
Air Pollutants/analysis , Air Pollution/analysis , Environmental Monitoring/methods , Gases/analysis , Particulate Matter/analysis , Biomass , COVID-19 , China , Dust/analysis , Environmental Monitoring/instrumentation , Environmental Pollution/analysis , Mass Spectrometry/instrumentation , Meteorology , Vehicle Emissions/analysis
11.
Environ Res ; 202: 111742, 2021 11.
Article in English | MEDLINE | ID: covidwho-1322095

ABSTRACT

This study aims to explore the real-time impact of the COVID-19 pandemic on measured air pollution in the three largest cities of Jordan (Amman, Irbid and Zarqa). It is hypothesized that a sharp decrease in the emitted amounts of particulate matter (PM10), CO, NO2 and SO2 during COVID-19 pandemic will be obtained, this corresponds with the reduced traffic due to mandated business closures. To achieve this exploration a paired sample t-test is used to compare the concentration of these four pollutants in the three cities over the period from 15 March to 30 June during the years from 2016 to 2020. It is found that there is a significant difference between the emitted concentrations mean values of CO, PM10, SO2 and NO2 during the period of study. This was indicated by the values of p for each species, which was less than 5 % for all these pollutants. The maximum reduction in SO2 and NO2 concentration during the lockdown period was in Zarqa. Irbid city witnessed the highest percentage reduction in CO and PM10. Furthermore, the correlation test, independent variable importance of multilayer perceptron and global sensitivity analysis using Sobol analysis showed that metrological data (Humidity, wind speed, average temperature and pressure) have a direct relationship with concentrations of CO, PM10, SO2 and NO2 in Amman, Irbid and Zarqa before and after COVID-19 pandemic.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Communicable Disease Control , Environmental Monitoring , Humans , Jordan/epidemiology , Meteorology , Pandemics , SARS-CoV-2
12.
Sci Rep ; 11(1): 15110, 2021 07 23.
Article in English | MEDLINE | ID: covidwho-1322504

ABSTRACT

The lockdown measures that were taken to combat the COVID-19 pandemic minimized anthropogenic activities and created natural laboratory conditions for studying air quality. Both observations and WRF-Chem simulations show a 20-50% reduction (compared to pre-lockdown and same period of previous year) in the concentrations of most aerosols and trace gases over Northwest India, the Indo Gangetic Plain (IGP), and the Northeast Indian regions. It is shown that this was mainly due to a 70-80% increase in the height of the boundary layer and the low emissions during lockdown. However, a 60-70% increase in the pollutants levels was observed over Central and South India including the Arabian sea and Bay of Bengal during this period, which is attributed to natural processes. Elevated (dust) aerosol layers are transported from the Middle East and Africa via long-range transport, and a decrease in the wind speed (20-40%) caused these aerosols to stagnate, enhancing the aerosol levels over Central and Southern India. A 40-60% increase in relative humidity further amplified aerosol concentrations. The results of this study suggest that besides emissions, natural processes including background meteorology and dynamics, play a crucial role in the pollution concentrations over the Indian sub-continent.


Subject(s)
Air Pollutants/analysis , Air Pollution/analysis , Environmental Monitoring/methods , Vehicle Emissions/analysis , Aerosols/analysis , Africa , Bays , COVID-19 , Communicable Disease Control , Correlation of Data , Dust/analysis , Environmental Pollution/analysis , Humans , India , Meteorology , Middle East , Oceans and Seas , Pandemics
13.
J Environ Manage ; 291: 112676, 2021 Aug 01.
Article in English | MEDLINE | ID: covidwho-1213353

ABSTRACT

Unprecedented travel restrictions due to the COVID-19 pandemic caused remarkable reductions in anthropogenic emissions, however, the Beijing area still experienced extreme haze pollution even under the strict COVID-19 controls. Generalized Additive Models (GAM) were developed with respect to inter-annual variations, seasonal cycles, holiday effects, diurnal profile, and the non-linear influences of meteorological factors to quantitatively differentiate the lockdown effects and meteorology impacts on concentrations of nitrogen dioxide (NO2) and fine particulate matters (PM2.5) at 34 sites in the Beijing area. The results revealed that lockdown measures caused large reductions while meteorology offset a large fraction of the decrease in surface concentrations. GAM estimates showed that in February, the control measures led to average NO2 reductions of 19 µg/m3 and average PM2.5 reductions of 12 µg/m3. At the same time, meteorology was estimated to contribute about 12 µg/m3 increase in NO2, thereby offsetting most of the reductions as well as an increase of 30 µg/m3 in PM2.5, thereby resulting in concentrations higher than the average PM2.5 concentrations during the lockdown. At the beginning of the lockdown period, the boundary layer height was the dominant factor contributing to a 17% increase in NO2 while humid condition was the dominant factor for PM2.5 concentrations leading to an increase of 65% relative to the baseline level. Estimated NO2 emissions declined by 42% at the start of the lockdown, after which the emissions gradually increased with the increase of traffic volumes. The diurnal patterns from the models showed that the peak of vehicular traffic occurred from about 12pm to 5pm daily during the strictest control periods. This study provides insights for quantifying the changes in air quality due to the lockdowns by accounting for meteorological variability and providing a reference in evaluating the effectiveness of control measures, thereby contributing to air quality mitigation policies.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Communicable Disease Control , Environmental Monitoring , Humans , Meteorology , Nitrogen Dioxide/analysis , Pandemics , Particulate Matter/analysis , SARS-CoV-2
14.
J Med Virol ; 93(2): 878-885, 2021 02.
Article in English | MEDLINE | ID: covidwho-1196409

ABSTRACT

The outbreak of novel pneumonia coronavirus disease has become a public health concern worldwide. Here, for the first time, the association between Korean meteorological factors and air pollutants and the COVID-19 infection was investigated. Data of air pollutants, meteorological factors, and daily COVID-19 confirmed cases of seven metropolitan cities and nine provinces were obtained from 3 February 2020 to 5 May 2020 during the first wave of pandemic across Korea. We applied the generalized additive model to investigate the temporal relationship. There was a significantly nonlinear association between daily temperature and COVID-19 confirmed cases. Each 1°C increase in temperature was associated with 9% (lag 0-14; OR = 1.09; 95% CI = 1.03-1.15) increase of COVID-19 confirmed cases when the temperature was below 8°C. A 0.01 ppm increase in NO2 (lag 0-7, lag 0.14, and lag 0-21) was significantly associated with increases of COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.02-1.25), 1.19 (1.09-1.30), and 1.30 (1.19-1.41), respectively. A 0.1 ppm increase in CO (lag 0-21) was associated with the increase in COVID-19 confirmed cases (OR = 1.10, 95% CI = 1.04-1.16). There was a positive association between per 0.001 ppm of SO2 concentration (lag 0, lag 0-7, and lag 0-14) and COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.04-1.22), 1.20 (1.11-1.31), and 1.15 (1.07-1.25), respectively. There were significantly temporal associations between temperature, NO2 , CO, and SO2 concentrations and daily COVID-19 confirmed cases in Korea.


Subject(s)
Air Pollutants/analysis , Air Pollution/analysis , COVID-19/epidemiology , Pandemics , Particulate Matter/analysis , SARS-CoV-2/pathogenicity , COVID-19/diagnosis , Carbon Monoxide/analysis , Cities/epidemiology , Humans , Meteorology/methods , Nitrogen Dioxide/analysis , Republic of Korea/epidemiology , Sulfur Dioxide/analysis , Temperature
15.
Sensors (Basel) ; 21(5)2021 Feb 27.
Article in English | MEDLINE | ID: covidwho-1121255

ABSTRACT

Due to the emergence of the coronavirus disease (COVID 19), education systems in most countries have adapted and quickly changed their teaching strategy to online teaching. This paper presents the design and implementation of a novel Internet of Things (IoT) device, called MEIoT weather station, which incorporates an exogenous disturbance input, within the National Digital Observatory of Smart Environments (OBNiSE) architecture. The exogenous disturbance input involves a wind blower based on a DC brushless motor. It can be controlled, via Node-RED platform, manually through a sliding bar, or automatically via different predefined profile functions, modifying the wind speed and the wind vane sensor variables. An application to Engineering Education is presented with a case study that includes the instructional design for the least-squares regression topic for linear, quadratic, and cubic approximations within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work's main contribution to the state-of-the-art is to turn a weather monitoring system into a hybrid hands-on learning approach thanks to the integrated exogenous disturbance input.


Subject(s)
Internet of Things/instrumentation , Meteorology/instrumentation , Weather , Computers
16.
Environ Res ; 195: 110854, 2021 04.
Article in English | MEDLINE | ID: covidwho-1065079

ABSTRACT

Although lockdown of the industrial and transport sector and stay at home advisories to counter the COVID-19 pandemic have shown that the air quality has improved during this time, very little is known about the role of ambient air pollutants and meteorology in facilitating its transmission. This paper presents the findings from a study that was conducted to evaluate whether air quality index (AQI), three primary pollutants (PM2.5, PM10 and CO), Ground level ozone (O3) and three meteorological variables (temperature, relative humidity, wind speed) have promoted the COVID-19 transmission in five megacities of India. The results show significant correlation of PM2.5, PM10, CO, O3 concentrations, AQI and meteorological parameters with the confirmed cases and deaths during the lockdown period. Among the meteorological variables considered, temperature strongly correlated with the COVID-19 cases and deaths during the lockdown (r=0.54;0.25) and unlock period (r=0.66;0.25). Among the pollutants, ozone, and among the meteorological variables, temperature, explained the highest variability, up to 34% and 30% respectively, for COVID-19 confirmed cases and deaths. AQI was not a significant parameter for explaining the variations in confirmed and death cases. WS and RH could explain 10-11% and 4-6% variations of COVID-19 cases. A GLM model could explain 74% and 35% variability for confirmed cases and deaths during the lockdown and 66% and 19% variability during the unlock period. The results suggest that meteorological parameters may have promoted the COVID-19 incidences, especially the confirmed cases. Our findings may encourage future studies to explore more about the role of ambient air pollutants and meteorology on transmission of COVID-19 and similar infectious diseases.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Cities , Communicable Disease Control , Humans , India/epidemiology , Meteorology , Pandemics , Particulate Matter/analysis , SARS-CoV-2
17.
J Environ Sci (China) ; 115: 422-431, 2022 May.
Article in English | MEDLINE | ID: covidwho-1046320

ABSTRACT

The national lockdown policies have drastically disrupted socioeconomic activities during the COVID-19 pandemic in China, which provides a unique opportunity to investigate the air quality response to such anthropogenic disruptions. And it is meaningful to evaluate the potential health impacts of air quality changes during the lockdown, especially for PM2.5 with adverse health effects. In this study, by using PM2.5 observations from 1388 monitoring stations nationwide in China, we examine the PM2.5 variations between the COVID-19 lockdown (February and March in 2020) and the same period in 2015-2019, and find that the national average of PM2.5 decreases by 18 µg/m3, and mean PM2.5 for most sites (about 75%) decrease by 30%-60%. The anthropogenic and meteorological contributions to these PM2.5 variations are also determined by using a stepwise multiple linear regression (MLR) model combined with the Kolmogorov-Zurbenko filter. Our results show that the change of anthropogenic emissions is a leading contributor to those widespread PM2.5 reductions, and meteorological conditions have the negative influence on PM2.5 reductions for some regions, such as Beijing-Tianjin-Hebei (BTH). Additionally, the avoided premature death due to PM2.5 reduction is estimated as a predicted number based on a log-linear concentration-response function. The total avoided premature death is 9952 in China, with dominant contribution (94%) from anthropogenic emission changes. For BTH, Yangtze River Delta, Pearl River Delta and Hubei regions, the reductions of PM2.5 are 24.1, 24.3, 13.5 and 29.5 µg/m3, with the avoided premature deaths of 1066, 1963, 454 and 583, respectively.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , China , Communicable Disease Control , Environmental Monitoring , Humans , Meteorology , Pandemics , Particulate Matter/analysis , SARS-CoV-2
18.
Sci Rep ; 10(1): 22112, 2020 12 17.
Article in English | MEDLINE | ID: covidwho-1038222

ABSTRACT

In January 2020, anthropogenic emissions in Northeast Asia reduced due to the COVID-19 outbreak. When outdoor activities of the public were limited, PM2.5 concentrations in China and South Korea between February and March 2020 reduced by - 16.8 µg/m3 and - 9.9 µg/m3 respectively, compared with the average over the previous three years. This study uses air quality modeling and observations over the past four years to separate the influence of reductions in anthropogenic emissions from meteorological changes and emission control policies on this PM2.5 concentration change. Here, we show that the impacts of anthropogenic pollution reduction on PM2.5 were found to be approximately - 16% in China and - 21% in South Korea, while those of meteorology and emission policies were - 7% and - 8% in China, and - 5% and - 4% in South Korea, respectively. These results show that the influence on PM2.5 concentration differs across time and region and according to meteorological conditions and emission control policies. Finally, the influence of reductions in anthropogenic emissions was greater than that of meteorological conditions and emission policies during COVID-19 period.


Subject(s)
Air Pollution/legislation & jurisprudence , COVID-19/prevention & control , Environmental Monitoring/legislation & jurisprudence , Meteorology/legislation & jurisprudence , Particulate Matter/analysis , Air Pollutants/adverse effects , Air Pollutants/analysis , Air Pollution/analysis , China , Environmental Pollution/prevention & control , Humans , Republic of Korea , SARS-CoV-2/pathogenicity , Vehicle Emissions/analysis
19.
20.
Environ Pollut ; 272: 115927, 2021 Mar 01.
Article in English | MEDLINE | ID: covidwho-893762

ABSTRACT

With the implementation of COVID-19 restrictions and consequent improvement in air quality due to the nationwide lockdown, ozone (O3) pollution was generally amplified in China. However, the O3 levels throughout the Guangxi region of South China showed a clear downward trend during the lockdown. To better understand this unusual phenomenon, we investigated the characteristics of conventional pollutants, the influence of meteorological and anthropogenic factors quantified by a multiple linear regression (MLR) model, and the impact of local sources and long-range transport based on a continuous emission monitoring system (CEMS) and the HYSPLIT model. Results show that in Guangxi, the conventional pollutants generally declined during the COVID-19 lockdown period (January 24 to February 9, 2020) compared with their concentrations during 2016-2019, while O3 gradually increased during the resumption (10 February to April 2020) and full operation periods (May and June 2020). Focusing on Beihai, a typical Guangxi region city, the correlations between the daily O3 concentrations and six meteorological parameters (wind speed, visibility, temperature, humidity, precipitation, and atmospheric pressure) and their corresponding regression coefficients indicate that meteorological conditions were generally conducive to O3 pollution mitigation during the lockdown. A 7.84 µg/m3 drop in O3 concentration was driven by meteorology, with other decreases (4.11 µg/m3) explained by reduced anthropogenic emissions of O3 precursors. Taken together, the lower NO2/SO2 ratios (1.25-2.33) and consistencies between real-time monitored primary emissions and ambient concentrations suggest that, with the closure of small-scale industries, residual industrial emissions have become dominant contributors to local primary pollutants. Backward trajectory cluster analyses show that the slump of O3 concentrations in Southern Guangxi could be partly attributed to clean air mass transfer (24-58%) from the South China Sea. Overall, the synergistic effects of the COVID-19 lockdown and meteorological factors intensified O3 reduction in the Guangxi region of South China.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Ozone , Air Pollutants/analysis , Air Pollution/analysis , China , Cities , Communicable Disease Control , Environmental Monitoring , Humans , Meteorology , Ozone/analysis , SARS-CoV-2
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