scholarly journals Associations of ambient air pollutants and meteorological factors with COVID-19 transmission in 31 Chinese provinces: A time-series study

2020 ◽  
Author(s):  
Han Cao ◽  
Bingxiao Li ◽  
Tianlun Gu ◽  
Xiaohui Liu ◽  
Kai Meng ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Meta Analysis ◽  
Ambient Air ◽  
Air Pollutant ◽  
Exposure Response ◽  
Ambient Air Pollutants ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

AbstractBackgroundEvidence regarding the effects of ambient air pollutants and meteorological factors on COVID-19 transmission is limited.ObjectivesTo explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases across 31 Chinese provinces during the outbreak period.MethodsThe number of COVID-19 confirmed cases, air pollutant concentrations and meteorological factors in 31 Chinese provinces from January 25 to February 29, 2020 were extracted from authoritative electronic databases. The associations were estimated for a single-day lag (lag0-lag6) as well as moving averages lag (lag01-lag05) using generalized additive mixed models (GAMMs), adjusted for time trends, day of the week, holidays and meteorological variables. Region-specific analyses and meta-analysis were conducted in five selected regions with diverse air pollution levels and weather conditions. Nonlinear exposure-response analyses were performed.ResultsWe examined 77,578 COVID-19 confirmed cases across 31 Chinese provinces during the study period. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3 and CO at lag4 corresponded to 1.40 (1.37-1.43), 1.35 (1.32-1.37), 1.01 (1.00-1.02), 1.08 (1.07-1.10), 1.28 (1.27-1.29) and 1.26 (1.24-1.28) odds ratios (ORs) of daily COVID-19 confirmed new cases, respectively. For 1 °C, 1% and 1 m/s increase in temperature, relative humidity and wind velocity, the ORs were 0.97 (0.97-0.98), 0.96 (0.96-0.97), and 0.94 (0.92-0.95), respectively. The estimates of PM2.5, PM10, NO2 and all meteorological factors remained statistically significant after meta-analysis for the five selected regions. The exposure-response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily COVID-19 confirmed new cases increasing.ConclusionsHigher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. As summer months are arriving in the Northern Hemisphere, the environmental factors and implementation of public health control measures may play an optimistic role in controlling COVID-19 epidemic.

scholarly journals Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110602
Author(s):  
Han Cao ◽  
Bingxiao Li ◽  
Tianlun Gu ◽  
Xiaohui Liu ◽  
Kai Meng ◽  
...  
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Wind Velocity ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Meta Analysis ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration–response analyses were performed. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3, and CO at lag4 corresponded to 1.40 (1.37–1.43), 1.35 (1.32–1.37), 1.01 (1.00–1.02), 1.08 (1.07–1.10), 1.28 (1.27–1.29), and 1.26 (1.24–1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97–0.98), 0.96 (0.96–0.97), and 0.94 (0.92–0.95), respectively. The estimates of PM2.5, PM10, NO2, and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration–response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM2.5, PM10, NO2, may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

Land use regression modelling of ambient air pollutants in Hong Kong

2020 ◽  
Author(s):  
Zhiyuan Li ◽  
Steve Hung Lam Yim ◽  
Kin-Fai Ho
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Air Quality ◽  
Air Pollutants ◽  
Ambient Air ◽  
Air Pollutant ◽  
High Rise ◽  
Pollutant Concentrations ◽  
Air Quality Measurements ◽  
Air Pollutant Concentrations

<p>Land use regression (LUR) models estimate air pollutant concentrations for areas without air quality measurements, which provides valuable information for exposure assessment and epidemiological studies. In the present study, we developed LUR models for ambient air pollutants in Hong Kong, China, a typical high-density and high-rise city. Air quality measurements at sixteen air quality monitoring stations, operated by the Hong Kong Environmental Protection Department, were collected. Moreover, five categories of predictor variables, including population distribution, traffic emissions, land use variables, urban/building morphology, and meteorological parameters, were employed to establish the LUR models of various air pollutants. Then the spatial distribution of air pollutant concentrations at 1 km × 1 km grid cells were plotted. Taking fine particle (PM2.5) as an example, the developed LUR model explained 89% of variability of PM2.5 concentrations, with a leave-one-out-cross-validation R2 of 0.64. LUR modelling results for other air pollutants will be presented. In addition, further improvements on the development of LUR models will be discussed. This study can help to assess long-term exposures to air pollutants for high-density and high-rise urban areas like Hong Kong.</p>

scholarly journals Effects of Water Removal Devices on Ambient Inorganic Air Pollutant Measurements

2019 ◽  
Vol 16 (18) ◽  
pp. 3446 ◽  
Author(s):  
Dong-June Kim ◽  
Trieu-Vuong Dinh ◽  
Joo-Yeon Lee ◽  
In-Young Choi ◽  
Dong-Jin Son ◽  
...  
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Air Pollutants ◽  
Ambient Air ◽  
Air Pollutant ◽  
Water Removal ◽  
Recovery Rates ◽  
Ambient Air Pollutants ◽  
Removal Efficiencies ◽  
Ambient Levels

Water vapor is a pivotal obstacle when measuring ambient air pollutants. The effects of water vapor removal devices which are called KPASS (Key-compound PASSer) and Cooler. On the measurement of O3, SO2, and CO at ambient levels were investigated. Concentrations of O3, SO2, and CO were 100 ppb, 150 ppb, and 25 ppm, respectively. The amount of water vapor varied at different relative humidity levels of 30%, 50%, and 80% when the temperature was 25 °C and the pressure was 1 atm. Water vapor removal efficiencies and recovery rates of target gases were also determined. The KPASS showed a better performance than the Cooler device, removing 93.6% of water vapor and the Cooler removing 59.2%. In terms of recovery, the KPASS showed a better recovery of target gases than the Cooler. Consequently, it is suggested that the KPASS should be an alternative way to remove water vapor when measuring O3, SO2, and CO.

scholarly journals Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

2020 ◽  
Vol 17 (6) ◽  
pp. 2025
Author(s):  
Laura Goulier ◽  
Bastian Paas ◽  
Laura Ehrnsperger ◽  
Otto Klemm
Keyword(s):  
Air Pollutants ◽  
Air Pollutant ◽  
Urban Air ◽  
Pollutant Concentrations ◽  
Day Of The Week ◽  
Artificial Neural ◽  
Input Variables ◽  
Air Pollutant Concentrations ◽  
Artificial Neural Network Ann ◽  
Performance Results

Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.

scholarly journals Analysis of Temporal Variations of Air Pollutant Concentrations in Ogoni Area, Niger Delta Region, Nigeria

2021 ◽  
pp. 63-73
Author(s):  
B. Yorkor ◽  
T. G. Leton ◽  
J. N. Ugbebor
Keyword(s):  
Air Pollution ◽  
Temporal Variability ◽  
Niger Delta ◽  
Air Pollutants ◽  
Temporal Variations ◽  
Time Of Day ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Hourly Data ◽  
Air Pollutant Concentrations

This study investigated the temporal variations of air pollutant concentrations in Ogoni area, Niger Delta, Nigeria. The study used hourly data measured over 8 hours for 12 months at selected locations within the area. The analyses were based on time series and time variations techniques in Openair packages of R programming software. The variations of air pollutant concentrations by time of day and days of week were simulated. Hours of the day, days of the week and monthly variations were graphically simulated. Variations in the mean concentrations of air pollutants by time were determined at 95 % confidence intervals. Sulphur dioxide (SO2), Nitrogen dioxide (NO2), ground level Ozone (O3) and fine particulate matter (PM2.5) concentrations exceeded permissible standards. Air pollutant concentrations showed increase in January, February, November and December compared to other months. Simulation showed that air pollutants varied significantly by hours-of-the-day and days-of-the-week and months-of-the-year. Analysis of temporal variability revealed that air pollutant concentrations increased during weekdays and decreased during weekends. The temporal variability of air pollutants in Ogoni area showed that anthropogenic activities were the main sources of air pollution in the area, therefore further studies are required to determine air pollutant dispersion pattern and evaluation the potential sources of air pollution in the area.

scholarly journals Intervention analysis with autoregressive integrated moving average models for time series of urban air pollutants

2021 ◽  
Vol 2139 (1) ◽  
pp. 012002
Author(s):  
L A Manco-Perdomo ◽  
L A Pérez-Padilla ◽  
C A Zafra-Mejía
Keyword(s):  
Air Pollutants ◽  
Moving Average ◽  
Air Pollutant ◽  
Urban Air ◽  
Intervention Analysis ◽  
Autoregressive Integrated Moving Average ◽  
Pollutant Concentrations ◽  
Moving Average Models ◽  
Regulatory Interventions ◽  
Air Pollutant Concentrations

Abstract The objective of this paper is to show an intervention analysis with autoregressive integrated moving average models for time series of air pollutants in a Latin American megacity. The interventions considered in this study correspond to public regulations for the control of urban air quality. The study period comprised 10 years. Information from 10 monitoring stations distributed throughout the megacity was used. Modelling showed that setting maximum emission limits for different pollution sources and improving fuel were the most appropriate regulatory interventions to reduce air pollutant concentrations. Modelling results also suggested that these interventions began to be effective between the first 4 days-15 days after their publication. The models developed on a monthly timescale had a short autoregressive memory. The air pollutant concentrations at a given time were influenced by the concentrations of up to three months immediately preceding. Moving average term of the models showed fluctuations in time of the air pollutant concentrations (3 months - 14 months). Within the framework of the applications of physics for the air pollution control, this study is relevant for the following findings: the usefulness of autoregressive integrated moving average models to temporal simulate air pollutants, and for its suitable performance to detect and quantify regulatory interventions.

scholarly journals Associations Between Ambient Air Pollutant Concentrations and Birth Weight

Epidemiology ◽  
2019 ◽  
Vol 30 (5) ◽  
pp. 624-632 ◽  
Author(s):  
Matthew J. Strickland ◽  
Ying Lin ◽  
Lyndsey A. Darrow ◽  
Joshua L. Warren ◽  
James A. Mulholland ◽  
...  
Keyword(s):  
Birth Weight ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

scholarly journals Ambient air pollutant concentrations during pregnancy and the risk of fetal growth restriction

2009 ◽  
Vol 63 (6) ◽  
pp. 488-496 ◽  
Author(s):  
D Q Rich ◽  
K Demissie ◽  
S-E Lu ◽  
L Kamat ◽  
D Wartenberg ◽  
...  
Keyword(s):  
Fetal Growth ◽  
Fetal Growth Restriction ◽  
Ambient Air ◽  
Air Pollutant ◽  
Growth Restriction ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

scholarly journals Aggravation of CoVID-19 infections due to air pollutant concentrations in Indian cities

2021 ◽  
Author(s):  
Arnab Mondal ◽  
Asha Sunilkumar ◽  
Shishir Kumar Singh ◽  
Surajit Mondal ◽  
Amit Kumar Mondal
Keyword(s):  
Air Pollutants ◽  
Time Frame ◽  
Air Pollutant ◽  
Pollution Levels ◽  
Pollutant Concentrations ◽  
The Government ◽  
Air Pollutant Concentrations ◽  
High Level ◽  
In The Beginning ◽  
Indian Cities

Abstract In the beginning of March 2020, cases of CoVID-19 infections began rising worldwide, reacting to which the Government of India called for nationwide lockdown initially for March 25th to April 14th, 2020 and later extended it in phases till May 31st, 2020. Due to the forced restrictions pan-India on every level, the move led to drastic drop in pollution levels. The results demonstrated that during lockdown air quality is significantly improved and the pollution levels of PM2.5, PM10, SO2 and NO2 reduced drastically during the lockdown period than the preceding year for the same time frame. A direct relationship has been established between the high level of air pollutants (PM2.5, PM10, NO2 and SO2) and CoVID-19 infections being reported in these Indian cities. The correlation indicates that the air pollutants like PM2.5, PM10, NO2 and SO2 are aggravating the number of casualties due to the CoVID-19 infections. The high-level exposure of PM2.5 over a long period is found to be significantly correlated with the mortality per unit confirmed CoVID-19 cases as compared to other air pollutant parameters like PM10, NO2 and SO2.

Application and evaluation of two model fusion approaches to obtain ambient air pollutant concentrations at a fine spatial resolution (250m) in Atlanta

2018 ◽  
Vol 109 ◽  
pp. 182-190 ◽  
Author(s):  
Josephine T. Bates ◽  
Audrey Flak Pennington ◽  
Xinxin Zhai ◽  
Mariel D. Friberg ◽  
Francesca Metcalf ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Ambient Air ◽  
Air Pollutant ◽  
Model Fusion ◽  
Pollutant Concentrations ◽  
Fine Spatial Resolution ◽  
Air Pollutant Concentrations
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