Meteorological Parameters and Gaseous Pollutant Concentrations as Predictors of Ground-level PM2.5 Concentrations in the Beijing-Tianjin-Hebei Region, China

Meteorological parameters and traffic flows have a direct impact on air quality in large urban areas, and hence on the quality of life in them. A large number of done surveys confirmed the great dependence of the concentration of ground-level ozone (O3) upon meteorological parameters and the size, structure and imbalances of traffic flows. As part of the research conducted in the period from November 5th to December 8th 2014 in Brcko in Muderis Ibrahimbegic St concentrations of ground-level ozone (O3) were measured, meteorological parameters (temperature, humidity, wind speed and intensity of solar radiation) and characteristics of traffic flow of road motor vehicles. The maximum concentrations of ground-level ozone (O3) in the measurement period was 106.54μg/m³, while the minimum concentration was 4.794μg/m³. By analyzing the results of measurements the high coefficient of correlation between wind speed, air temperature and humidity was established. The correlation coefficient between the traffic flows on the one hand and the concentration of ground-level ozone (O3), on the other hand is very low and does not exceed the value of 0.301. A negative correlation coefficient between traffic flows and concentrations of ground-level ozone (O3) is also observed in the certain time of the day.

Download Full-text

Estimating ground level PM2.5 concentrations and associated health risk in India using satellite based AOD and WRF predicted meteorological parameters

Chemosphere ◽

10.1016/j.chemosphere.2020.126969 ◽

2020 ◽

Vol 255 ◽

pp. 126969 ◽

Cited By ~ 5

Author(s):

Shovan Kumar Sahu ◽

Shubham Sharma ◽

Hongliang Zhang ◽

Venkatesh Chejarla ◽

Hao Guo ◽

...

Keyword(s):

Health Risk ◽

Meteorological Parameters ◽

Ground Level

Download Full-text

A Machine Learning Approach to Predict Air Quality in California

Complexity ◽

10.1155/2020/8049504 ◽

2020 ◽

Vol 2020 ◽

pp. 1-23 ◽

Cited By ~ 2

Author(s):

Mauro Castelli ◽

Fabiana Martins Clemente ◽

Aleš Popovič ◽

Sara Silva ◽

Leonardo Vanneschi

Keyword(s):

Machine Learning ◽

Air Quality ◽

Environmental Protection Agency ◽

Urban Areas ◽

Principal Component ◽

Ground Level ◽

Support Vector ◽

Validation Data ◽

Pollutant Concentrations ◽

Rbf Kernel

Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data.

Download Full-text

Dew on reindeer lichen

Canadian Journal of Forest Research ◽

10.1139/x91-198 ◽

1991 ◽

Vol 21 (9) ◽

pp. 1415-1418 ◽

Cited By ~ 3

Author(s):

Gyula Péch

Keyword(s):

Moisture Content ◽

Meteorological Parameters ◽

Ground Level ◽

Meteorological Station ◽

Mass Measurements ◽

Moisture Contents ◽

Hourly Rate ◽

Simple Linear Relationship ◽

Maximum Moisture Content ◽

Reindeer Lichen

Four reindeer lichen (Cladinarangiferina (L.) Nyl) samples were placed near ground level in the open at a meteorological station where dew and other meteorological parameters were measured. One sample was covered occasionally from sunset to sunrise to prevent dew and to evaluate moisture gain due to wetting by atmospheric vapour alone. Mass measurements were done day and night following a set schedule. At the conclusion of the field program the samples were oven-dried and all weight measurements were converted to moisture contents. The results showed that a simple linear relationship adequately describes the overnight rise of lichen moisture caused by dew, and that atmospheric vapour alone, on nights without rain or condensation, can raise lichen moisture by 15%. Further, the results confirmed that nocturnal moisture gains by either dew or atmospheric vapour dissipate on subsequent clear mornings by noon. These findings suggest that at locations where humidity is measured at night and dew may be assessed visually in the morning, one can estimate both the 06:00 maximum moisture content of the lichen and, on subsequent clear mornings, the hourly rate of its drying.

Download Full-text

WRF-Chem Modeling of Summertime Air Pollution in the Northern Great Plains: Chemistry and Aerosol Mechanism Intercomparison

Atmosphere ◽

10.3390/atmos12091121 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1121

Author(s):

Carlos J. Bucaram ◽

Frank M. Bowman

Keyword(s):

Great Plains ◽

Oil And Gas ◽

Ground Level ◽

Gas Production ◽

Northern Great Plains ◽

Baseline Scenario ◽

Oil And Gas Production ◽

Monthly Average ◽

Pollutant Concentrations ◽

Rural Study

Oil and gas production in the Bakken region increased dramatically during the past decade. A WRF-Chem modeling study of the Northern Great Plains was conducted for a July 2010 baseline scenario prior to the largest of these production increases. Simulations using the RACM-MADE/SORGAM, CBMZ-MOSAIC, and MOZART-MOSAIC chemistry-aerosol mechanisms were compared to each other and against ground level observations. All three gas-aerosol modules produced similar prediction results for O3, and NO2, with moderate correlation to hourly measurements and monthly average values overpredicted by 20% for O3 and underpredicted by 5% for NO2. Monthly average PM2.5 concentrations were relatively accurate, but correlation to hourly measurements was very low and PM2.5 subspecies exhibited high variability with a mix of over and underpredictions depending on the mechanism. Pollutant concentrations were relatively low across the mostly rural study domain, especially in the Bakken region. Results from this work can be used as a basis of comparison for studies of more recent time periods that include increased oil and gas-related emissions.

Download Full-text

Contrasting Trends of Surface PM2.5, O3, and NO2 and Their Relationships with Meteorological Parameters in Typical Coastal and Inland Cities in the Yangtze River Delta

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182312471 ◽

2021 ◽

Vol 18 (23) ◽

pp. 12471

Author(s):

Min Lv ◽

Zhanqing Li ◽

Qingfeng Jiang ◽

Tianmeng Chen ◽

Yuying Wang ◽

...

Keyword(s):

Yangtze River ◽

Meteorological Parameters ◽

Yangtze River Delta ◽

Air Pollutant ◽

River Delta ◽

Control Measures ◽

The Yangtze River ◽

Pollutant Concentrations ◽

Extreme High Temperature ◽

The Yangtze River Delta

The contrasting trends of surface particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) and their relationships with meteorological parameters from 2015 to 2019 were investigated in the coastal city of Shanghai (SH) and the inland city of Hefei (HF), located in the Yangtze River Delta (YRD). In both cities, PM2.5 declined substantially, while O3 and NO2 showed peak values during 2017 when the most frequent extreme high-temperature events occurred. Wind speed was correlated most negatively with PM2.5 and NO2 concentrations, while surface temperature and relative humidity were most closely related to O3. All of the studied pollutants were reduced by rainfall scavenging, with the greatest reduction seen in PM2.5, followed by NO2 and O3. By contrast, air pollutants in the two cities were moderately strongly correlated, although PM2.5 concentrations were much lower and Ox (O3 + NO2) concentrations were higher in SH. Additionally, complex air pollution hours occurred more frequently in SH. Air pollutant concentrations changed more with wind direction in SH. A more effective washout effect was observed in HF, likely due to the more frequent strong convection and thunderstorms in inland areas. This research suggests pertinent air quality control measures should be designed accordingly for specific geographical locations.

Download Full-text

Daily Concentrations of PM2.5 in the Valencian Community Using Random Forest for the Period 2008–2018

Proceedings ◽

10.3390/proceedings2019019013 ◽

2019 ◽

Vol 19 (1) ◽

pp. 13

Author(s):

Represa ◽

Palomar-Vázquez ◽

Porta ◽

Fernández-Sarría

Keyword(s):

Climate Change ◽

Random Forest ◽

Cross Validation ◽

Meteorological Parameters ◽

Fine Particulate Matter ◽

Ground Level ◽

Test Set ◽

Quality Models ◽

Final Model ◽

Fine Particulate

Fine particulate matter (PM2.5) is a global problem that affects the population health and contributes to climate change. Remote sensing provides useful information for the development of air quality models. This work aims to obtain a daily model of PM2.5 levels in the Valencian Community with a resolution of 1 km for the period 2008–2018. MODIS-MAIAC images, meteorological parameters of the MERRA-2 project, land cover information and ground level measurements of PM2.5 levels were analysed with Random Forest. The verification of the model was carried out using cross-validation repeated ten times, and an evaluation of a test set with 20% of the collected information. The final model was used to generate maps of the daily concentrations of PM2.5 for the area of the Valencian Community throughout the study period.

Download Full-text

Assessment of the impact of traffic volume on NO2 levels in Munich during the lock-down periods

10.5194/dach2022-161 ◽

2021 ◽

Author(s):

Sheng Ye ◽

Ka Lok Chan ◽

Tamara Brunner ◽

Hanlin Zhang ◽

Alexander Geiß ◽

...

Keyword(s):

Meteorological Parameters ◽

Early Stage ◽

Measurement Techniques ◽

Absolute Humidity ◽

Ground Level ◽

Traffic Volume ◽

Impact Factors ◽

Long Term Study ◽

The Impact

The global pandemic has many negative economic, social and health impacts, but the lock-downs also led to a reduction of traffic volume which resulted in lower NO2 levels in some areas. Our study made use of different air quality measurement techniques (in-situ, on-road, satellite remote sensing) to monitor long-term NO2 levels in Munich. While comparing NO2 levels associate with traffic volume before and after a lock-down, other influences based on meteorological parameters should be considered as well. In addition to traffic data we used records of wind, mixing layer height, temperature, humidity and other meteorological parameters to analyze the impact on measured pollution levels using a Generalized Additive Model (GAM) regression. Our long-term study using data between 2018 and 2021 shows that the dominating factor is wind speed, followed by traffic volume as the main factors for impacting NO2 levels, while absolute humidity and wind direction show less effects. We utilized those findings to find best suited time periods comparable to the lockdown time in terms of meteorological conditions. In order to focus on the traffic volume factor, we applied these findings to minimize other impact factors to evaluate the NO2 variability of different years comparing to the data from the lockdown periods. A significant reduction of the ground level NO2 concentrations in Munich during the early stage of the lockdown period in March 2020 could clearly be associated with a significant reduction of traffic volume.

Download Full-text

The role of meteorological conditions and pollution control strategies in reducing air pollution in Beijing during APEC 2014 and Victory Parade 2015

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-13921-2017 ◽

2017 ◽

Vol 17 (22) ◽

pp. 13921-13940 ◽

Cited By ~ 33

Author(s):

Pengfei Liang ◽

Tong Zhu ◽

Yanhua Fang ◽

Yingruo Li ◽

Yiqun Han ◽

...

Keyword(s):

Air Pollution ◽

Pollution Control ◽

Air Pollutants ◽

Meteorological Parameters ◽

Control Strategies ◽

Meteorological Conditions ◽

Air Pollutant ◽

Pollutant Concentrations ◽

Air Pollutant Concentrations

Abstract. To control severe air pollution in China, comprehensive pollution control strategies have been implemented throughout the country in recent years. To evaluate the effectiveness of these strategies, the influence of meteorological conditions on levels of air pollution needs to be determined. Using the intensive air pollution control strategies implemented during the Asia-Pacific Economic Cooperation Forum in 2014 (APEC 2014) and the 2015 China Victory Day Parade (Victory Parade 2015) as examples, we estimated the role of meteorological conditions and pollution control strategies in reducing air pollution levels in Beijing. Atmospheric particulate matter of aerodynamic diameter  ≤ 2.5 µm (PM2.5) samples were collected and gaseous pollutants (SO2, NO, NOx, and O3) were measured online at a site in Peking University (PKU). To determine the influence of meteorological conditions on the levels of air pollution, we first compared the air pollutant concentrations during days with stable meteorological conditions. However, there were few days with stable meteorological conditions during the Victory Parade. As such, we were unable to estimate the level of emission reduction efforts during this period. Finally, a generalized linear regression model (GLM) based only on meteorological parameters was built to predict air pollutant concentrations, which could explain more than 70 % of the variation in air pollutant concentration levels, after incorporating the nonlinear relationships between certain meteorological parameters and the concentrations of air pollutants. Evaluation of the GLM performance revealed that the GLM, even based only on meteorological parameters, could be satisfactory to estimate the contribution of meteorological conditions in reducing air pollution and, hence, the contribution of control strategies in reducing air pollution. Using the GLM, we found that the meteorological conditions and pollution control strategies contributed 30 and 28 % to the reduction of the PM2.5 concentration during APEC and 38 and 25 % during the Victory Parade, respectively, based on the assumption that the concentrations of air pollutants are only determined by meteorological conditions and emission intensities. We also estimated the contribution of meteorological conditions and control strategies in reducing the concentrations of gaseous pollutants and PM2.5 components with the GLMs, revealing the effective control of anthropogenic emissions.

Download Full-text