scholarly journals Organizing bicycle traffic in Moscow to reduce air pollutant concentrations

2020 ◽  
Vol 164 ◽  
pp. 04007
Author(s):  
Igor Pryadko

The objective of this article is to assess the prospects for development of cycling as a mode of transport in major cities in Russia and worldwide. Towards this end, the author addresses bicycle traffic organization patterns in the cities of Europe, South Eastern Asia and South America. The methods, employed in this research project, include sociological data collection, or the polling of urban residents (residents of the Russian capital), the retrospective analysis of sources, including news articles, the comparative historical method and forecasting. In the article, the impact produced on the urban environment, namely, on the surface layers of the urban atmosphere, by the motor traffic is compared with the one produced by the bicycle traffic. The mission of this research project is to analyze development of cycling network routes, parking lots, and accompanying small architectural forms in Moscow. The author employs methods of environmental monitoring to assess the impact produced by the motor transport on the environmental situation in the city. The conclusion is that there is a need to develop the urban walking infrastructure, to expand the urban cycling network, and to convert to the biosphere compatible urban transport.

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Fengzhu Tan ◽  
Weijie Wang ◽  
Sufen Qi ◽  
Haidong Kan ◽  
Xinpei Yu ◽  
...  

Abstract Background Many studies have reported the impact of air pollution on cardiovascular disease (CVD), but few of these studies were conducted in severe haze-fog areas. The present study focuses on the impact of different air pollutant concentrations on daily CVD outpatient visits in a severe haze-fog city. Methods Data regarding daily air pollutants and outpatient visits for CVD in 2013 were collected, and the association between six pollutants and CVD outpatient visits was explored using the least squares mean (LSmeans) and logistic regression. Adjustments were made for days of the week, months, air temperature and relative humidity. Results The daily CVD outpatient visits for particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) in the 90th-quantile group were increased by 30.01, 29.42, 17.68, 14.98, 29.34%, and − 19.87%, respectively, compared to those in the <10th-quantile group. Odds ratios (ORs) and 95% confidence intervals (CIs) for the increase in daily CVD outpatient visits in PM10 300- and 500-μg/m3, PM2.5 100- and 300-μg/m3 and CO 3-mg/m3 groups were 2.538 (1.070–6.020), 7.781 (1.681–36.024), 3.298 (1.559–6.976), 8.72 (1.523–49.934), and 5.808 (1.016–33.217), respectively, and their corresponding attributable risk percentages (AR%) were 60.6, 87.15, 69.68, 88.53 and 82.78%, respectively. The strongest associations for PM10, PM2.5 and CO were found only in lag 0 and lag 1. The ORs for the increase in CVD outpatient visits per increase in different units of the six pollutants were also analysed. Conclusions All five air pollutants except O3 were positively associated with the increase in daily CVD outpatient visits in lag 0. The high concentrations of PM10, PM2.5 and CO heightened not only the percentage but also the risk of increased daily CVD outpatient visits. PM10, PM2.5 and CO may be the main factors of CVD outpatient visits.


Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1490
Author(s):  
Zhihua Su ◽  
Xin Li ◽  
Yunlong Liu ◽  
Bing Deng

The lockdown during the coronavirus disease 2019 (COVID-19) pandemic provides a scarce opportunity to assess the efficiency of air pollution mitigation. Herein, the monitoring data of air pollutants were thoroughly analyzed together with meteorological parameters to explore the impact of human activity on the multi-time scale changes of air pollutant concentrations in Guiyang city, located in Southwest China. The results show that the COVID-19 lockdown had different effects on the criteria air pollutants, i.e., PM2.5 (diameter ≤ 2.5 μm), PM10 (diameter ≤ 10 μm), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) concentrations. The lockdown caused a significant drop in NO2 concentration. During the first-level lockdown period, the NO2 concentration declined sharply by 8.41 μg·m−3 (45.68%). The decrease in NO concentration caused the “titration effect” to weaken, leading to a sharp increase in O3 concentration. Although human activities resumed partially and the “titration effect” enhanced certainly during the second-level lockdown period, the meteorological conditions became more conducive to the formation of O3 by photochemical reactions. Atmosphere oxidation was enhanced to promote the generation of secondary aerosols through gas–particle transitions, thus compensating for the reduced primary emission of PM2.5. The implication of this study is that the appropriate air pollution control policies must be initiated to suppress the secondary generation of both PM2.5 and O3.


Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3264
Author(s):  
Aurelia Rybak ◽  
Aleksandra Rybak

This article presents the research on the analysis of the impact of social isolation caused by the COVID-19 pandemic on gaseous air pollutant concentrations. For this purpose, the authors presented (thermal maps) and analyzed the concentrations of selected gases such as NO2, CO, SO2, and PM2.5 particles during the strict quarantine period in Poland and other EU countries. Statistical analysis of the concentration level of these gases was performed. It was noticed that in Poland, Germany, and France, the concentrations of such gases as CO, NO2, and PM2.5 particles decreased, while in Italy and Spain, the tendency was the opposite. To verify whether the discovered dependencies are not a natural continuation of the trends shaping the given phenomenon, the time series of gas and PM2.5 particle emissions were analyzed. On this basis, the emission forecast up to 2023 was created, using the ARIMA class models. The obtained results allowed to construct five scenarios for the development of NO2, CO, SO2, and PM2.5 emissions until 2023, considering the impact of the COVID-19 pandemic. It was stated that in the optimistic scenario, in 2023, a decrease in CO, NO2, and PM2.5 emissions could be achieved by maximums of 51%, 95%, and 28%, respectively.


2015 ◽  
Vol 28 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Thomas Bersinger ◽  
Isabelle Le Hécho ◽  
Gilles Bareille ◽  
Thierry Pigot ◽  
Alexandre Lecomte

Continuous monitoring of the sanitation network of the urban catchment of Pau (southwest France) has been performed since March 2012 using rain gauges, flowmeters, as well as turbidity and conductivity probes. Good correlations were obtained between turbidity, total suspended solids (TSS) and chemical oxygen demand (COD) on the one hand, and conductivity and total nitrogen on the other hand. This allowed an instantaneous and continuous estimation of pollutant concentrations and fluxes since that date. In the present paper we focused on the results of October 2012, which was characterized by alternating periods of dry and rainy events. Turbidity and conductivity raw data show different trends during the study period depending on the parameter and the rain events. A turbidity peak is observed at the beginning of each rain event but its amplitude varies with the intensity of the rain and the length of the preceding dry weather period. Conversely, conductivity decrease during each rain event implying, that rain water acts as a dilution factor. The behaviour of COD and total nitrogen differ markedly due to their partitioning between the dissolved (total nitrogen) and particulate phases (COD). Daily pollutant fluxes allow a global comprehension and monitoring of the sewer system. Important COD fluxes during a rain event preceded by a long dry weather period highlight the importance of erosion of sedimentary deposits in the sewerage network. During these events, important fluxes are discharged into receiving water leading to the question of the impact on aquatic life. Generally, these results highlight the potential of online monitoring to better understand the behaviour of the sewer network on long or short time scales. This could be a useful tool to manage wastewater treatment.


Author(s):  
Laura Goulier ◽  
Bastian Paas ◽  
Laura Ehrnsperger ◽  
Otto Klemm

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.


Author(s):  
B. Yorkor ◽  
T. G. Leton ◽  
J. N. Ugbebor

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.


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