Analyzing the Contribution of Human Mobility to Changes in Air Pollutants: Insights from the COVID-19 Lockdown in Wuhan

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions in air pollutants, we implemented sensitivity experiments by Random Forest (RF) models, with the comparison of the contributions of meteorological conditions, human mobility, and emissions from industry and households between different periods. In addition, we conducted scenario analyses to suggest an appropriate limit for control of human mobility. Different mechanisms for air pollutants were shown in the pre-pandemic, pre-lockdown, lockdown, and post-pandemic periods. Wind speed and the Within-city Migration index, representing intra-city mobility intensity, were excluded from stepwise multiple linear models in the pre-lockdown and lockdown periods. The results of sensitivity experiments show that, in the COVID-19 lockdown period, 73.3% of the reduction can be attributed to decreased human mobility. In the post-pandemic period, meteorological conditions control about 42.2% of the decrease, and emissions from industry and households control 40.0%, while human mobility only contributes 17.8%. The results of the scenario analysis suggest that the priority of restriction should be given to human mobility within the city than other kinds of human mobility. The reduction in the NO2 concentration tends to be smaller when human mobility within the city decreases by more than 70%. A limit of less than 40% on the control of the human mobility can achieve a better effect, especially in cities with severe traffic pollution.

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Residuals in the modelling of pollution concentration depending on meteorological conditions and traffic flow, employing decision trees

ITM Web of Conferences ◽

10.1051/itmconf/20182300016 ◽

2018 ◽

Vol 23 ◽

pp. 00016 ◽

Cited By ~ 3

Author(s):

Joanna A. Kamińska

Keyword(s):

Random Forest ◽

Traffic Flow ◽

Goodness Of Fit ◽

Regression Trees ◽

Meteorological Conditions ◽

Boosted Regression Trees ◽

Significant Preference ◽

Explanatory Variables ◽

The City

Two data mining methods – a random forest and boosted regression trees – were used to model values of roadside air pollution depending on meteorological conditions and traffic flow, using the example of data obtained in the city of Wrocław in the years 2015–2016. Eight explanatory variables – five continuous and three categorical – were considered in the models. A comparison was made of the quality of the fit of the models to empirical data. Commonly used goodness-of-fit measures did not imply a significant preference for either of the methods. Residual analysis was also performed; this showed boosted regression trees to be a more effective method for predicting typical values in the modelling of NO2, NOx and PM2.5, while the random forest method leads to smaller errors when predicting peaks.

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The Transfer Phenomena of Air Pollutants Emitted from Power Station and Generators in Nasiriyah City and the Effect of Meteorological Parameters on its Dispersion by Using Fixed Box Model

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/961/1/012001 ◽

2022 ◽

Vol 961 (1) ◽

pp. 012001

Author(s):

Ahmed Alaa Hussein ◽

Zahraa S. Mahdi ◽

Nagam Obaid Kariem

Keyword(s):

Wind Speed ◽

Air Pollutants ◽

Meteorological Parameters ◽

Emission Rate ◽

Electrical Energy ◽

Fuel Oil ◽

Box Model ◽

Upstream Direction ◽

Light Oil ◽

The City

Abstract The study aims to use the fixed box model to calculate the spread of pollutants (CO2, SO2, NOX, particulate) resulting from the burning of fuel used to produce electrical energy in the Nasiriyah city and to know the way they spread in the city through being affected by the wind speed and compare the results calculated from the model with the results measured by the lancom4 device. The results showed that the main pollutants for the air in Nasiriyah was emitted from burning the fuel used for the production of electric power, and the results showed that the concentration of pollutants (CO2, SO2, NOX) was much higher inside the city when compared with the upstream direction of the winds due to its increase with the movement of winds and its entry into the city. Through the application of the fixed box model and when comparing the calculated results through the model with the results measured by the lancom4 device, the error rate was (4 %, 2%, 2%, 5%) for pollutants (CO2, SO2, NOX, particulate) respectively, it was also observed that the highest emission rate of pollutants was result from using heavy fuel (fuel oil) and the lowest emission was from light oil (Dry gas). We noted the spread of pollutants and dilution in the atmosphere increases with the increase in wind speed, excluding for particles mater.

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Environmental Assessment of the Impact of Meteorological Parameters on Man-Made Pollution by Carcinogenicly Dangerous Chemicals in the Voronezh Air Basin

Ecology and Industry of Russia ◽

10.18412/1816-0395-2021-2-60-65 ◽

2021 ◽

Vol 25 (2) ◽

pp. 60-65

Author(s):

S.A. Kurolap ◽

V.S. Petrosyan ◽

O.V. Klepikov ◽

V.V. Kulnev ◽

D.Yu. Martynov

Keyword(s):

Wind Speed ◽

Air Temperature ◽

Meteorological Parameters ◽

Carcinogenic Risk ◽

Meteorological Conditions ◽

Air Humidity ◽

Maximum Permissible Level ◽

Relative Air Humidity ◽

The Impact ◽

The City

Based on the analysis of official statistics from the Voronezh Hydrometeorological Service, the patterns of the dynamics of pollutants (formaldehyde and soot) are investigated depending on the combination of various meteorological parameters — air temperature, wind speed, relative air humidity. A positive relationship has been established between the increase in atmospheric pollution with formaldehyde and air temperature. With increasing wind speed and relative humidity, the concentration of formaldehyde and soot in the atmosphere of the city, as a rule, decrease. The maximum permissible level of carcinogenic risk to public health has been established, causing concern. The obtained patterns can be used to predict the level of technogenic pollution of the city’s atmosphere, depending on meteorological conditions.

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Spread of COVID-19, Meteorological Conditions and Air Quality in the City of Buenos Aires, Argentina: Two Facets Observed during Its Pandemic Lockdown

Atmosphere ◽

10.3390/atmos11101045 ◽

2020 ◽

Vol 11 (10) ◽

pp. 1045 ◽

Cited By ~ 1

Author(s):

Tomás R. Bolaño-Ortiz ◽

Romina M. Pascual-Flores ◽

S. Enrique Puliafito ◽

Yiniva Camargo-Caicedo ◽

Lucas L. Berná-Peña ◽

...

Keyword(s):

Air Quality ◽

Buenos Aires ◽

Human Mobility ◽

Meteorological Conditions ◽

Decision Makers ◽

Temperature Correlation ◽

Daily Data ◽

Increased Risk ◽

Average Relative Humidity ◽

The City

This work studied the spread of COVID-19, the meteorological conditions and the air quality in a megacity from two viewpoints: (1) the correlation between meteorological and air quality (PM10 and NO2) variables with infections and deaths due COVID-19, and (2) the improvement in air quality. Both analyses were performed for the pandemic lockdown due to COVID-19 in the City of Buenos Aires (CABA), the capital and the largest city in Argentina. Daily data from temperature, rainfall, average relative humidity, wind speed, PM10, NO2, new cases and deaths due COVID-19 were analyzed. Our findings showed a significant correlation of meteorological and air quality variables with COVID-19 cases. The highest temperature correlation occurred before the confirmation day of new cases. PM10 presented the highest correlation within 13 to 15 days lag, while NO2 within 3 to 6 days lag. Also, reductions in PM10 and NO2 were observed. This study shows that exposure to air pollution was significantly correlated with an increased risk of becoming infected and dying due to COVID-19. Thus, these results show that the NO2 and PM10 levels in CABA can serve as one of the indicators to assess vulnerability to COVID-19. In addition, decision-makers can use this information to adopt strategies to restrict human mobility during the COVID-19 pandemic and future outbreaks of similar diseases in CABA.

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Analyzing the Contribution of Road Traffic to Changes of Air Pollutants Using Random Forest: Insights from COVID-19 lockdown in Wuhan

10.20944/preprints202108.0024.v1 ◽

2021 ◽

Author(s):

Jiansheng Wu ◽

Yun Qian ◽

Yuan Wang ◽

Na Wang

Keyword(s):

Air Quality ◽

Random Forest ◽

Industrial Production ◽

Human Activities ◽

Air Pollutants ◽

Road Traffic ◽

Emission Sources ◽

Migration Index ◽

The Road ◽

The City

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions of air pollutants, sensitivity experiments were implemented by random forest (RF) model, with the comparison of contributions of meteorology, road traffic, and emission sources between different periods. Besides, an emulator was operated to suggest an appropriate limit for control of transportation. The RF models showed different mechanisms for air pollutants. Within-city Migration index (WMI) was more important in the normal, pre-lockdown and post-pandemic model while Out-Migration Index (OMI) was emphasized in the lockdown model. In the COVID-19 lockdown period, 73.3% of the reduction can be attributed to the decreased road traffic, showing massive impact of road traffic on the air quality. In the post-pandemic period, meteorology controlled about 42.2% of the decrease and emissions from industry and household controlled 40.0% while road traffic only contributed to 17.8%. It was suggested that priority of restriction should be given to road traffic within the city. A limit of less than 40% on the control of the road traffic can get a better effect, especially for cities with severe traffic pollution.

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Meteorological Conditions and Covid-19 in Large U.S. Cities

10.1101/2020.05.17.20104547 ◽

2020 ◽

Author(s):

Hisato Takagi ◽

Toshiki Kuno ◽

Yujiro v ◽

Hiroki Ueyama ◽

Takuya Matsushiro ◽

...

Keyword(s):

Wind Speed ◽

Regression Line ◽

Meteorological Conditions ◽

Mean Temperature ◽

Mean Wind Speed ◽

The Mean ◽

Meta Regression ◽

Regression Graph ◽

Lower Temperature ◽

The City

To determine whether prevalence of Coronavirus disease 2019 (Covid-19) is modulated by meteorological conditions, we herein conducted meta-regression of data in large U.S. cities. We selected 33 large U.S. cities with a population of >500,000. The integrated numbers of confirmed Covid-19 cases in the country to which the city belongs on 14 May 2020, the estimated population in 2019 in the country, and monthly meteorological conditions at the city for 4 months (from January to April 2020) were obtained. Meteorological conditions consisted of mean temperature (F), total precipitation (inch), mean wind speed (MPH), mean sky cover, and mean relative humidity (%). Monthly data for 4 months were averaged or integrated. The Covid-19 prevalence was defined as the integrated number of Covid-19 cases divided by the population. Random-effects meta-regression was performed by means of OpenMetaAnalyst. In a meta-regression graph, Covid-19 prevalence (plotted as the logarithm transformed prevalence on the y-axis) was depicted as a function of a given factor (plotted as a meteorological datum on the x-axis). A slope of the meta-regression line was significantly negative (coefficient, -0.069; P < 0.001) for the mean temperature and significantly positive for the mean wind speed (coefficient, 0.174; P = 0.027) and the sky cover (coefficient, 2.220; P = 0.023). In conclusion, lower temperature and higher wind speed/sky cover may be associated with higher Covid-19 prevalence, which should be confirmed by further epidemiological researches adjusting for various risk and protective factors (in addition to meteorological conditions) of Covid-19.

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METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

Informatization and communication ◽

10.34219/2078-8320-2019-10-3-71-76 ◽

2019 ◽

pp. 71-76

Author(s):

A.E. Semenov

Keyword(s):

Random Forest ◽

Develop Model ◽

Random Forest Algorithm ◽

Saint Petersburg ◽

Pedestrian Navigation ◽

Factors Influencing ◽

The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

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Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics (Preprint)

10.2196/preprints.19331 ◽

2020 ◽

Cited By ~ 1

Author(s):

Mario Coccia

Keyword(s):

Air Pollution ◽

Wind Speed ◽

Critical Role ◽

Meteorological Condition ◽

Meteorological Conditions ◽

Lessons Learned ◽

Transmission Dynamics ◽

Critical Threshold ◽

Viral Infectivity ◽

Low Wind Speed

BACKGROUND Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death. OBJECTIVE This study has two goals. The first is to explain the main factors determining the diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats with of accelerated viral infectivity in society. METHODS Correlation and regression analyses on on data of N=55 Italian province capitals, and data of infected individuals at as of April 2020. RESULTS The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society. CONCLUSIONS Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19. CLINICALTRIAL not applicable

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Scenario Analyses of Exhaust Emissions Reduction through the Introduction of Electric Vehicles into the City

Energies ◽

10.3390/en14072030 ◽

2021 ◽

Vol 14 (7) ◽

pp. 2030

Author(s):

Marianna Jacyna ◽

Renata Żochowska ◽

Aleksander Sobota ◽

Mariusz Wasiak

Keyword(s):

Inner City ◽

Electric Vehicles ◽

Transport Model ◽

Exhaust Emissions ◽

Scenario Analyses ◽

High Emission ◽

Clean Air ◽

The One ◽

The City

In recent years, policymakers of urban agglomerations in various regions of the world have been striving to reduce environmental pollution from harmful exhaust and noise emissions. Restrictions on conventional vehicles entering the inner city are being introduced and the introduction of low-emission measures, including electric ones, is being promoted. This paper presents a method for scenario analysis applied to study the reduction of exhaust emissions by introducing electric vehicles in a selected city. The original scenario analyses relating to real problems faced by contemporary metropolitan areas are based on the VISUM tool (PTV Headquarters for Europe: PTV Planung Transport Verkehr AG, 76131 Karlsruhe, Germany). For the case study, the transport model of the city of Bielsko-Biala (Poland) was used to conduct experiments with different forms of participation of electric vehicles on the one hand and traffic restrictions for high emission vehicles on the other hand. Scenario analyses were conducted for various constraint options including inbound, outbound, and through traffic. Travel time for specific transport relations and the volume of harmful emissions were used as criteria for evaluating scenarios of limited accessibility to city zones for selected types of vehicles. The comparative analyses carried out showed that the introduction of electric vehicles in the inner city resulted in a significant reduction in the emission of harmful exhaust compounds and, consequently, in an increase in the area of clean air in the city. The case study and its results provide some valuable insights and may guide decision-makers in their actions to introduce both driving ban restrictions for high-emission vehicles and incentives for the use of electric vehicles for city residents.

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