COVID-19 and the Environment, Review and Analysis

We reviewed studies linking COVID-19 cases and deaths with the environment, focusing on relationships with air pollution. We found both short- and long-term observational relationships with a range of regulated pollutants, although only two studies considered both cases (i.e., infections) and deaths within a common analytical framework. Most of these studies were limited to a few months of the pandemic period. Statistically significant relationships were found more often for PM2.5 and NO2 than for other regulated pollutants, but no rationale was suggested for such short-term relationships; latency was seldom considered for long-term relationships. It was also unclear whether confounding had been adequately controlled in either type of study. Studies of air quality improvement following lockdowns found more robust relationships with local (CO, NO2) rather than regional (PM2.5, O3) pollutants, but meteorological confounding was seldom considered. Only one of seven studies of airborne virus transmission reported actual measurements. Overall, we found the existing body of literature to be more suggestive than definitive. Due to these various deficiencies, we assembled a new state-level database of cumulative COVID-19 cases and deaths through March 2021 with a range of potential predictor variables and performed linear regression analyses on various combinations. As single predictors, we found significant (p < 0.05) relationships between cumulative cases and household crowding (+), education (−), face-mask usage (−), or voting Republican (+). For cumulative deaths, we found significant relationships with education (−), black race (+), or previous levels of PM2.5 (+). NOx (+), and elemental carbon (EC, +). We found no relationships between long-term air quality and cumulative COVID-19 cases. Our associations linking air pollution with COVID-19 mortality were not statistically different from those for all-cause mortality in previous studies. In multiple mortality regressions combining air pollution, race, and education, NOx and EC remained significant but PM2.5 did not. We concluded that the current worldwide emphasis on PM2.5 is misplaced. We predicted air pollutant effects of a few percentage points, but individual differences between races, political identification, and post-graduate education were of the order of factors of 2 to 4. In general, the factors predicting infection were personal and related to COVID-19 exposure, while those predicting subsequent mortality tended to be more situational and related to geography. Overall, we concluded that how you live is more important than where you live.

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Development of a 3D Real-Time Atmospheric Monitoring System (3DREAMS) Using Doppler LiDARs and Applications for Long-Term Analysis and Hot-and-Polluted Episodes

Remote Sensing ◽

10.3390/rs12061036 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1036 ◽

Cited By ~ 6

Author(s):

Steve Hung Lam YIM

Keyword(s):

Air Pollution ◽

Hong Kong ◽

Air Quality ◽

Real Time ◽

Monitoring System ◽

Air Pollutant ◽

Vertical Wind ◽

Horizontal Wind ◽

Atmospheric Monitoring

Heatwaves and air pollution are serious environmental problems that adversely affect human health. While related studies have typically employed ground-level data, the long-term and episodic characteristics of meteorology and air quality at higher altitudes have yet to be fully understood. This study developed a 3-Dimensional Real-timE Atmospheric Monitoring System (3DREAMS) to measure and analyze the vertical profiles of horizontal wind speed and direction, vertical wind velocity as well as aerosol backscatter. The system was applied to Hong Kong, a highly dense city with complex topography, during each season and including hot-and-polluted episodes (HPEs) in 2019. The results reveal that the high spatial wind variability and wind characteristics in the lower atmosphere in Hong Kong can extend upwards by up to 0.66 km, thus highlighting the importance of mountains for the wind environment in the city. Both upslope and downslope winds were observed at one site, whereas downward air motions predominated at another site. The high temperature and high concentration of fine particulate matter during HPEs were caused by a significant reduction in both horizontal and vertical wind speeds that established conditions favorable for heat and air pollutant accumulation, and by the prevailing westerly wind promoting transboundary air pollution. The findings of this study are anticipated to provide valuable insight for weather forecasting and air quality studies. The 3DREAMS will be further developed to monitor upper atmosphere wind and air quality over the Greater Bay Area of China.

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Association between long-term exposure to ambient air pollutants and mortality rates because of circulatory and respiratory diseases in South Korea

10.21203/rs.2.23984/v1 ◽

2020 ◽

Author(s):

Jinhee Kwon ◽

Jeongeun Hwang ◽

Hahn Yi ◽

Hyun-Jin Bae ◽

Miso Jang ◽

...

Keyword(s):

Air Pollution ◽

Heart Disease ◽

Air Quality ◽

Air Pollutants ◽

Respiratory Diseases ◽

Mortality Rates ◽

Quality Standard ◽

Air Pollutant ◽

Pm 10

Abstract Background : Associations between long-term exposure to common air pollutants including nitrogen dioxide, carbon monoxide, sulfur dioxide (SO 2 ), ozone, and particulate matter (PM 10 ) and health consequences have been studied. We investigated spatial effects of exposure to air pollution on mortality by circulatory and respiratory diseases nation-wide and in metropolitan. Methods: Means of daily concentration of the common air pollutants from 2005 to 2016 were calculated by district unit using linear interpolation. Age-standardized mortality rates of people suffering from heart disease (HD); cerebrovascular disease (CVD); ischemic heart disease (IHD); pneumonia (PN) and chronic lower respiratory disease (CLRD) were acquired from population census data. Sub-divided comparisons were performed to adjust spatial heterogeneity. Pearson’s correlation coefficients between mortality rates and air pollutant concentrations were investigated. Multivariable linear regressions were performed to investigate associations considering confounding factors. Results: Air pollutant concentration in metropolitan was the highest, except SO 2 ; in particular, PM 10 concentration was higher than air quality standard (PM 10 : 55.27 µg/m 3 , air quality standard: 50.00 µg/m 3 , P<0.05). Pearson’s correlation coefficient between PM 10 and mortality rates was significant ( r =0.313, 0.596, 0.420, -0.277 and 0.523 for HD, CVD, IHD, PN, and CLRD, all P<0.05) in metropolitan. The powers of regression model for PM 10 , smoking rate, education level, and population density were 0.532 and 0.482 (adjusted R 2 ) for mortality rates of CVD and CLRD, respectively. Conclusion : Long-term exposure study with sub-divided analysis showed overall associations between air pollution exposure and circulatory and respiratory disease mortalities. PM 10 exposure was significantly associated with mortality of CVD and CLRD in metropolitan.

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Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Atmosphere ◽

10.3390/atmos12060788 ◽

2021 ◽

Vol 12 (6) ◽

pp. 788

Author(s):

Rong Feng ◽

Hongmei Xu ◽

Zexuan Wang ◽

Yunxuan Gu ◽

Zhe Liu ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Rural Areas ◽

Vehicle Emissions ◽

Motor Vehicle ◽

Northwest China ◽

Air Pollutant ◽

Capital City ◽

Air Pollution Control ◽

Motor Vehicle Emissions

In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

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Long-Term Exposure to PM2.5 and Cognitive Decline: A Longitudinal Population-Based Study

Journal of Alzheimer s Disease ◽

10.3233/jad-200852 ◽

2021 ◽

pp. 1-9

Author(s):

Giulia Grande ◽

Jing Wu ◽

Petter L.S. Ljungman ◽

Massimo Stafoggia ◽

Tom Bellander ◽

...

Keyword(s):

Air Pollution ◽

Cognitive Decline ◽

Population Based ◽

Cerebrovascular Diseases ◽

Air Pollutant ◽

National Study ◽

Piecewise Regression ◽

Risk Increase ◽

Restricted Cubic Splines

Background: A growing but contrasting evidence relates air pollution to cognitive decline. The role of cerebrovascular diseases in amplifying this risk is unclear. Objectives: 1) Investigate the association between long-term exposure to air pollution and cognitive decline; 2) Test whether cerebrovascular diseases amplify this association. Methods: We examined 2,253 participants of the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). One major air pollutant (particulate matter ≤2.5μm, PM2.5) was assessed yearly from 1990, using dispersion models for outdoor levels at residential addresses. The speed of cognitive decline (Mini-Mental State Examination, MMSE) was estimated as the rate of MMSE decline (linear mixed models) and further dichotomized into the upper (25%fastest cognitive decline), versus the three lower quartiles. The cognitive scores were used to calculate the odds of fast cognitive decline per levels of PM2.5 using regression models and considering linear and restricted cubic splines of 10 years exposure before the baseline. The potential modifier effect of cerebrovascular diseases was tested by adding an interaction term in the model. Results: We observed an inverted U-shape relationship between PM2.5 and cognitive decline. The multi-adjusted piecewise regression model showed an increased OR of fast cognitive decline of 81%(95%CI = 1.2–3.2) per interquartile range difference up to mean PM2.5 level (8.6μg/m3) for individuals older than 80. Above such level we observed no further risk increase (OR = 0.89;95%CI = 0.74–1.06). The presence of cerebrovascular diseases further increased such risk by 6%. Conclusion: Low to mean PM2.5 levels were associated with higher risk of accelerated cognitive decline. Cerebrovascular diseases further amplified such risk.

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Gender Differences in Association between Air Pollution and Daily Mortality in the Capital of the Green Lungs of Poland–Population-Based Study with 2,953,000 Person-Years of Follow-Up

Journal of Clinical Medicine ◽

10.3390/jcm9082351 ◽

2020 ◽

Vol 9 (8) ◽

pp. 2351

Author(s):

Łukasz Kuźma ◽

Krzysztof Struniawski ◽

Szymon Pogorzelski ◽

Hanna Bachórzewska-Gajewska ◽

Sławomir Dobrzycki

Keyword(s):

Public Health ◽

Gender Differences ◽

Air Pollution ◽

Air Quality ◽

Air Pollutant ◽

World Health ◽

The European Union ◽

Daily Mortality ◽

Male Population ◽

The Impact

(1) Introduction: air pollution is considered to be one of the main risk factors for public health. According to the European Environment Agency (EEA), air pollution contributes to the premature deaths of approximately 500,000 citizens of the European Union (EU), including almost 5000 inhabitants of Poland every year. (2) Purpose: to assess the gender differences in the impact of air pollution on the mortality in the population of the city of Bialystok—the capital of the Green Lungs of Poland. (3) Materials and Methods: based on the data from the Central Statistical Office, the number—and causes of death—of Białystok residents in the period 2008–2017 were analyzed. The study utilized the data recorded by the Provincial Inspectorate for Environmental Protection station and the Institute of Meteorology and Water Management during the analysis period. Time series regression with Poisson distribution was used in statistical analysis. (4) Results: A total of 34,005 deaths had been recorded, in which women accounted for 47.5%. The proportion of cardiovascular-related deaths was 48% (n = 16,370). An increase of SO2 concentration by 1-µg/m3 (relative risk (RR) 1.07, 95% confidence interval (CI) 1.02–1.12; p = 0.005) and a 10 °C decrease of temperature (RR 1.03, 95% CI 1.01–1.05; p = 0.005) were related to an increase in the number of daily deaths. No gender differences in the impact of air pollution on mortality were observed. In the analysis of the subgroup of cardiovascular deaths, the main pollutant that was found to have an effect on daily mortality was particulate matter with a diameter of 2.5 μm or less (PM2.5); the RR for 10-µg/m3 increase of PM2.5 was 1.07 (95% CI 1.02–1.12; p = 0.01), and this effect was noted only in the male population. (5) Conclusions: air quality and atmospheric conditions had an impact on the mortality of Bialystok residents. The main air pollutant that influenced the mortality rate was SO2, and there were no gender differences in the impact of this pollutant. In the male population, an increased exposure to PM2.5 concentration was associated with significantly higher cardiovascular mortality. These findings suggest that improving air quality, in particular, even with lower SO2 levels than currently allowed by the World Health Organization (WHO) guidelines, may benefit public health. Further studies on this topic are needed, but our results bring questions whether the recommendations concerning acceptable concentrations of air pollutants should be stricter, or is there a safe concentration of SO2 in the air at all.

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Regional Features of Long-Term Exposure to PM2.5 Air Quality over Asia under SSP Scenarios Based on CMIP6 Models

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18136817 ◽

2021 ◽

Vol 18 (13) ◽

pp. 6817

Author(s):

Sungbo Shim ◽

Hyunmin Sung ◽

Sanghoon Kwon ◽

Jisun Kim ◽

Jaehee Lee ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

East Asia ◽

Climate Model ◽

Fine Particulate Matter ◽

Coupled Model ◽

Surface Air Temperature ◽

Quality Controls ◽

Quality Guidelines

This study investigates changes in fine particulate matter (PM2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated that the estimated present-day PM2.5 concentrations were comparable to satellite-derived data. Overall, the PM2.5 concentrations of the analyzed regions exceeded the WHO air-quality guidelines, particularly in East Asia and South Asia. In future SSP scenarios that consider the implementation of significant air-quality controls (SSP1-2.6, SSP5-8.5) and medium air-quality controls (SSP2-4.5), the annual PM2.5 levels were predicted to substantially reduce (by 46% to around 66% of the present-day levels) in East Asia, resulting in a significant improvement in the AQI values in the mid-future. Conversely, weak air pollution controls considered in the SSP3-7.0 scenario resulted in poor AQI values in China and India. Moreover, a predicted increase in the percentage of aged populations (>65 years) in these regions, coupled with high AQI values, may increase the risk of premature deaths in the future. This study also examined the regional impact of PM2.5 mitigations on downward shortwave energy and surface air temperature. Our results revealed that, although significant air pollution controls can reduce long-term exposure to PM2.5, it may also contribute to the warming of near- and mid-future climates.

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Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

Atmospheric Measurement Techniques ◽

10.5194/amt-10-3575-2017 ◽

2017 ◽

Vol 10 (9) ◽

pp. 3575-3588 ◽

Cited By ~ 83

Author(s):

Eben S. Cross ◽

Leah R. Williams ◽

David K. Lewis ◽

Gregory R. Magoon ◽

Timothy B. Onasch ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Lower Cost ◽

Electrochemical Sensors ◽

Air Pollutant ◽

Sensor System ◽

Time Interval ◽

High Dimensional Model Representation ◽

Integrated Sensor ◽

Mean Square Errors

Abstract. The environments in which we live, work, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality (AQ), measurements must be fast (real time), scalable, and reliable (with known accuracy, precision, and stability over time). Lower-cost air-quality-sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. This limits our ability to inform the public about pollution sources and inspire policy makers to address environmental justice issues related to air quality. In this paper, initial results obtained with a recently developed lower-cost air-quality-sensor system are reported. In this project, data were acquired with the ARISense integrated sensor package over a 4.5-month time interval during which the sensor system was co-located with a state-operated (Massachusetts, USA) air quality monitoring station equipped with reference instrumentation measuring the same pollutant species. This paper focuses on validating electrochemical (EC) sensor measurements of CO, NO, NO2, and O3 at an urban neighborhood site with pollutant concentration ranges (parts per billion by volume, ppb; 5 min averages, ±1σ): [CO]  =  231 ± 116 ppb (spanning 84–1706 ppb), [NO]  =  6.1 ± 11.5 ppb (spanning 0–209 ppb), [NO2]  =  11.7 ± 8.3 ppb (spanning 0–71 ppb), and [O3]  =  23.2 ± 12.5 ppb (spanning 0–99 ppb). Through the use of high-dimensional model representation (HDMR), we show that interference effects derived from the variable ambient gas concentration mix and changing environmental conditions over three seasons (sensor flow-cell temperature  =  23.4 ± 8.5 °C, spanning 4.1 to 45.2 °C; and relative humidity  =  50.1 ± 15.3 %, spanning 9.8–79.9 %) can be effectively modeled for the Alphasense CO-B4, NO-B4, NO2-B43F, and Ox-B421 sensors, yielding (5 min average) root mean square errors (RMSE) of 39.2, 4.52, 4.56, and 9.71 ppb, respectively. Our results substantiate the potential for distributed air pollution measurements that could be enabled with these sensors.

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Case Study on Anthropogenic Air Pollution in Nagpur City

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.34082 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 2279-2282

Author(s):

Mukul Dayaramani

Keyword(s):

Air Pollution ◽

Air Quality ◽

Harmful Effect ◽

Central India ◽

Well Being ◽

Good Health ◽

Air Pollutant ◽

Corporate Community ◽

Health And Well Being

Air pollution is a very serious problem worldwide. Anthropogenic air pollution is mostly related to the combustion of various types of fuels. Air pollutant levels remain too high and air quality problems are still not solved. The presence of pollutants in the air has a harmful effect on the human health and the environment. Good air quality is a prerequisite for our good health and well-being. Nagpur city is located in Maharashtra state of central India. Business hub and increased industrialization in study area is affecting the environment adversely. n. Changing life style of corporate community and their effects on other population enhancing the contamination of environment

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A Layered Recurrent Neural Network for Imputing Air Pollutants Missing Data and Prediction of NO 2, O 3, PM 10, and PM 2.5

10.5772/intechopen.93678 ◽

2020 ◽

Author(s):

Hamza Turabieh ◽

Alaa Sheta ◽

Malik Braik ◽

Elvira Kovač-Andrić

Keyword(s):

Neural Network ◽

Air Pollution ◽

Air Quality ◽

Recurrent Neural Network ◽

Missing Values ◽

Prediction Models ◽

Air Pollutant ◽

Forecasting Models ◽

Monitoring Strategies ◽

Artificial Neural Network Ann

To fulfill the national air quality standards, many countries have created emissions monitoring strategies on air quality. Nowadays, policymakers and air quality executives depend on scientific computation and prediction models to monitor that cause air pollution, especially in industrial cities. Air pollution is considered one of the primary problems that could cause many human health problems such as asthma, damage to lungs, and even death. In this study, we present investigated development forecasting models for air pollutant attributes including Particulate Matters (PM2.5, PM10), ground-level Ozone (O3), and Nitrogen Oxides (NO2). The dataset used was collected from Dubrovnik city, which is located in the east of Croatia. The collected data has missing values. Therefore, we suggested the use of a Layered Recurrent Neural Network (L-RNN) to impute the missing value(s) of air pollutant attributes then build forecasting models. We adopted four regression models to forecast air pollutant attributes, which are: Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) and L-RNN. The obtained results show that the proposed method enhances the overall performance of other forecasting models.

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EURODELTA-Trends, a multi-model experiment of air quality hindcast in Europe over 1990–2010

Geoscientific Model Development ◽

10.5194/gmd-10-3255-2017 ◽

2017 ◽

Vol 10 (9) ◽

pp. 3255-3276 ◽

Cited By ~ 19

Author(s):

Augustin Colette ◽

Camilla Andersson ◽

Astrid Manders ◽

Kathleen Mar ◽

Mihaela Mircea ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Boundary Conditions ◽

Radiative Forcing ◽

Regional Scale ◽

Air Pollutant ◽

Pollutant Emissions ◽

Mitigation Measures ◽

Modelling Experiment ◽

Technical Features

Abstract. The EURODELTA-Trends multi-model chemistry-transport experiment has been designed to facilitate a better understanding of the evolution of air pollution and its drivers for the period 1990–2010 in Europe. The main objective of the experiment is to assess the efficiency of air pollutant emissions mitigation measures in improving regional-scale air quality. The present paper formulates the main scientific questions and policy issues being addressed by the EURODELTA-Trends modelling experiment with an emphasis on how the design and technical features of the modelling experiment answer these questions. The experiment is designed in three tiers, with increasing degrees of computational demand in order to facilitate the participation of as many modelling teams as possible. The basic experiment consists of simulations for the years 1990, 2000, and 2010. Sensitivity analysis for the same three years using various combinations of (i) anthropogenic emissions, (ii) chemical boundary conditions, and (iii) meteorology complements it. The most demanding tier consists of two complete time series from 1990 to 2010, simulated using either time-varying emissions for corresponding years or constant emissions. Eight chemistry-transport models have contributed with calculation results to at least one experiment tier, and five models have – to date – completed the full set of simulations (and 21-year trend calculations have been performed by four models). The modelling results are publicly available for further use by the scientific community. The main expected outcomes are (i) an evaluation of the models' performances for the three reference years, (ii) an evaluation of the skill of the models in capturing observed air pollution trends for the 1990–2010 time period, (iii) attribution analyses of the respective role of driving factors (e.g. emissions, boundary conditions, meteorology), (iv) a dataset based on a multi-model approach, to provide more robust model results for use in impact studies related to human health, ecosystem, and radiative forcing.

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