scholarly journals Monitoring Of Co, No2 And So2 Levels During The Covid-19 Pandemic In Iran Using Remote Sensing Imagery

2019 ◽  
Author(s):  
N. M. Sari ◽  
M. N. S. Kuncoro
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Urban Areas ◽  
Google Earth ◽  
Population Activity ◽  
Climate Conditions ◽  
Pollution Levels ◽  
Before And After ◽  
Big Cities ◽  
The Government

The COVID-19 pandemic has had a major impact on various sectors. Iran is one of the countries most affected by this pandemic. After considering the huge impact, the government imposed strict rules prohibiting social gatherings and restricting travel for the entire population following the large number of victims in the country. These restrictions lead to changes in the environment, especially air quality. The purpose of this study was to find out how the COVID-19 pandemic affected air quality in Iran following the activity restrictions in the region. The method used in this research was based on the use of multitemporal Sentinel-5P data processing with scripts available on the Google Earth Engine applied on the images, acquired in the period before and after the COVID-19 pandemic. The data used included the image collection of Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide, Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide and Sentinel-5P NRTI SO2: Near Real-Time Sulphur Dioxide. The results showed, that for Iran in general, changes in the concentration of CO are clearly visible in urban areas with high population activity such as Tehran, where there was a decrease from 0.05 to 0.0286 mol/m2, while for other areas it is also influenced by the varying climate conditions, which affect the level of pollution. For the NO2 pollutant, there was a significant decrease in pollution levels in big cities such as Tehran, Qom, Isfahan and Mashhad from 0.0002 to 0.000114 mol/m2. For the SO2 pollutant, there was a decrease in pollution levels in Iran’s big cities from 0.0005 to 0.0000714 mol/m2. For Tehran province, which is the most populous and busiest province in Iran, it can be observed that there was also a decrease in the concentration of pollutants after the lockdown compared to the pre-lockdown period. The CO concentration decreased from 0.043 to 0.036 mol/m2, while for the NO2 pollutant there was a decrease from 0.0002 to 0.000142 mol/m2 and for the SO2 pollutant, there was a decrease from 0.0005 to 0.000143 mol/m2.

scholarly journals Environmental Vulnerability by Floods and Occupation of Permanent Preservation Area: An Approach about Three Cities of Midwest of Brazil

2017 ◽  
Vol 10 (2) ◽  
pp. 45
Author(s):  
Greyce Bernardes de Mello Rezende ◽  
Telma Lucia Bezerra Alves
Keyword(s):  
Urban Areas ◽  
Local Level ◽  
Urban Expansion ◽  
Google Earth ◽  
Satellite Imaging ◽  
Environmental Vulnerability ◽  
Industrial Buildings ◽  
Araguaia River ◽  
The Government ◽  
Very High

The purpose of this article is to identify the areas of environmental vulnerability by flooding in urban areas of the municipalities of Barra dos Garças - MT, Pontal do Araguaia - MT and Aragarças - GO; and demarcate the occupations in permanent preservation areas (PPAs) in the study area. The methodology uses variables such as time series of maximum quotas of the Araguaia River, from 1968 to 2014, the frequency of those floods, as well as the local level curves. From the junction of these data, it was stipulated the levels of environmental vulnerability by floods in five levels: very high, high, medium, low and very low. The results indicate that areas with very high vulnerability correspond to approximately 1,58 square kilometers which equals to 0.5% of the total area studied; the high vulnerability areas, have only 3.19 square kilometers, corresponding to 1% of the area; the medium vulnerability areas have 7.66 square kilometers, which corresponds to 2.41% of the area; low vulnerability areas, have 11.18 square kilometers of extension relating to 3.52% of the area; and finally the remainder of the study area was characterized as very low vulnerability. After this mapping, it was found by satellite imaging from Google earth software dated 2014, the main occupations in PPAs. The main uses and occupations refer to human activities related to tourism, as well as commercial, residential and industrial buildings. It was found that it is of salutary importance that the Government enforces the fulfillment of the restrictions set out in the Forest Code, preventing that more occupations occur in PPAs and areas subject to flooding. Moreover, the mapping of areas of flooding is also a tool for future public policies that aim to guide the recommended areas to urban expansion, as well as ordering the use and occupation of land by developing zoning.

scholarly journals Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models

2021 ◽  
Vol 21 (9) ◽  
pp. 7373-7394
Author(s):  
Jérôme Barré ◽  
Hervé Petetin ◽  
Augustin Colette ◽  
Marc Guevara ◽  
Vincent-Henri Peuch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Gradient Boosting ◽  
Quality Models ◽  
Pollution Levels ◽  
Surface Station ◽  
Concentration Changes ◽  
Monitoring Service ◽  
No2 Pollution

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

scholarly journals Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

2019 ◽  
Vol 11 (21) ◽  
pp. 2492 ◽  
Author(s):  
Bo Peng ◽  
Zonglin Meng ◽  
Qunying Huang ◽  
Caixia Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Real Time ◽  
Spectral Reflectance ◽  
Urban Areas ◽  
Flood Mapping ◽  
Urban Flooding ◽  
Urban Flood ◽  
Before And After ◽  
Flood Maps

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

scholarly journals Changes in regional meteorology induced by anthropogenic heat and their impacts on air quality in South China

2016 ◽  
Vol 16 (23) ◽  
pp. 15011-15031 ◽  
Author(s):  
Min Xie ◽  
Kuanguang Zhu ◽  
Tijian Wang ◽  
Wen Feng ◽  
Da Gao ◽  
...  
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
South China ◽  
Urban Areas ◽  
Air Pollutants ◽  
Surface Wind ◽  
Vertical Movement ◽  
Anthropogenic Heat ◽  
Big Cities ◽  
The Impact

Abstract. Anthropogenic heat (AH) emissions from human activities can change the urban circulation and thereby affect the air pollution in and around cities. Based on statistic data, the spatial distribution of AH flux in South China is estimated. With the aid of the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem), in which the AH parameterization is developed to incorporate the gridded AH emissions with temporal variation, simulations for January and July in 2014 are performed over South China. By analyzing the differences between the simulations with and without adding AH, the impact of AH on regional meteorology and air quality is quantified. The results show that the regional annual mean AH fluxes over South China are only 0.87 W m−2, but the values for the urban areas of the Pearl River Delta (PRD) region can be close to 60 W m−2. These AH emissions can significantly change the urban heat island and urban-breeze circulations in big cities. In the PRD city cluster, 2 m air temperature rises by 1.1° in January and over 0.5° in July, the planetary boundary layer height (PBLH) increases by 120 m in January and 90 m in July, 10 m wind speed is intensified to over 0.35 m s−1 in January and 0.3 m s−1 in July, and accumulative precipitation is enhanced by 20–40 % in July. These changes in meteorological conditions can significantly impact the spatial and vertical distributions of air pollutants. Due to the increases in PBLH, surface wind speed and upward vertical movement, the concentrations of primary air pollutants decrease near the surface and increase in the upper levels. But the vertical changes in O3 concentrations show the different patterns in different seasons. The surface O3 concentrations in big cities increase with maximum values of over 2.5 ppb in January, while O3 is reduced at the lower layers and increases at the upper layers above some megacities in July. This phenomenon can be attributed to the fact that chemical effects can play a significant role in O3 changes over South China in winter, while the vertical movement can be the dominant effect in some big cities in summer. Adding the gridded AH emissions can better describe the heterogeneous impacts of AH on regional meteorology and air quality, suggesting that more studies on AH should be carried out in climate and air quality assessments.

scholarly journals Brick Sector and Air Quality: An Integrated Assessment towards 2020 Challenge of Environment Development

2021 ◽  
Vol 19 (2) ◽  
pp. 153-164
Author(s):  
Shazia Pervaiz ◽  
◽  
Muhammad Ameer Nawaz Akram ◽  
Filza Zafar Khan ◽  
Kanwal Javid ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Air Quality ◽  
Winter Season ◽  
Environmental Parameters ◽  
Control Measures ◽  
Pollution Levels ◽  
Brick Kilns ◽  
Central Regions ◽  
The Government ◽  
Set Up

Brick sector is a mainstay of the urban economy of Punjab. The traditional technology of brick making emits a lot of toxic gases and smoke particulates into air. Hence, the Government of the Punjab, Pakistan announced a ban on low technology brick kiln operations during winter season by the end of December 2020. Initially, the existing set up of brick kilns and air pollution levels were evaluated before and during lockdown period using spatial application. Further, environmental parameters such as aerosols, carbon monoxide, ozone, sulfur dioxide and carbon dioxide were determined to analyze the air quality, including metrological factors. Results of the study exhibited that the upper and central regions of Punjab are the major hubs of brick kilns. So, the level of air quality was inconsistent in the study period due to the existence of large mushrooms of brick kilns. Further, despite lockdown the highest concentration of carbon monoxide was recorded in the eastern side of the province, such as Kasur, Lahore, and Sheikhupura. The level of aerosols also fluctuated and shifted its trends in the central and southern part of the province. While SO2 and CO2 level declined and revealed a satisfactory level of air quality during shutdown. On the other hand, no significant relation to metrological factors, such as rain, is involved in the pollution reduction. Conclusively, the findings of the present study encourage the government agencies to realign the stringent control measures to improve the quality of air in the winter months using the experience of quarantine in 2020.

Building spatial-temporal NO2 land use regression models in complex urban environment

2020 ◽  
Author(s):  
Peng Wei ◽  
Yang Xing ◽  
Li Sun ◽  
Zhi Ning
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Air Quality ◽  
Urban Environment ◽  
Real Time ◽  
Urban Areas ◽  
Personal Exposure ◽  
Pollutant Emission ◽  
Land Use Regression ◽  
Pollutant Distribution

&lt;p&gt;Air quality and traffic-related pollutants in urban areas are major concerns especially in meg-cities. Current Air Quality Monitoring Station (AQMS) cannot sufficiently reveal these pollution conditions with limited point measurements and limited information cannot supply adequate insight on personal exposure in a complex urban environment. Land Use Regression (LUR) model provided a feasible solution for estimating outdoor personal exposure by adding multiple data sources. However, fixed-site passive monitoring still lacks enough spatial coverage or spatial flexibility to estimate pollutant distribution at the fine-scale level.&lt;/p&gt;&lt;p&gt;A Mobile Air Sensor Network (MASEN) project was deployed in the Hong Kong area, with electrochemical gas sensors installed on the routine buses to capture on-road NO&lt;sub&gt;x&lt;/sub&gt; pollutant measurement, the data was collected by the integrated sensor system and transfer to the database for real-time visualization. Compared with previous mobile measurements used for LUR model building which limited to 1-2 routes, this measurement covered major roads in the Hong Kong area and get an overview of pollutant distribution at various ambient. Two main variables were introduced to improve the model performance: 1) Sky View Factor (SVF) which represented pollutant dispersion status were obtained from Google street view image, a deep learning model was used for scene parsing to recognized targets in this procedure, 2) a Real-time Traffic Congestion Index (RTCI) which represented traffic pollutants emission was obtained from Google map and merged with road network. A common LUR model will be built based on a distance-decay regression selection strategy for variables selection. Meanwhile, a spatial-temporal LUR model will be built which contained both diurnal variability and day-to-day variability. Finally, a high-resolution pollution map of the urban areas will illustrate NO&lt;sub&gt;2&lt;/sub&gt; pollutant distribution.&lt;/p&gt;&lt;p&gt;In this work, we aimed at estimating traffic-related pollutants in a complex city environment and identifying hotspots at both spatial and temporal aspects. Meanwhile, the novel data source which closely associated with traffic-related pollutant emission also gives a better understanding of guidance on urban planning.&lt;/p&gt;

scholarly journals Preliminary Results of Air Pollution Status in Selected Roadsides in Jalingo, Taraba State Nigeria

2021 ◽  
Vol 5 (3) ◽  
pp. 118-123
Author(s):  
Maxwell Obia Kanu ◽  
Terkaa Victor Targema ◽  
Gideon Maumee Abednego
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Ambient Air ◽  
Acceptable Limit ◽  
Pollution Levels ◽  
The Mean ◽  
Carbon Dioxide Co2 ◽  
Mean Concentration

The rapid increase in vehicular activities in the past two centuries contributes vastly to air pollution levels. In as much as Social interactions and economic growth are well enhanced by vehicular transportation in many developing countries, it is unfortunate that exhausts from vehicles contribute immensely to ambient air quality especially in the urban areas. The concentrations of carbon monoxides (CO) and carbon dioxide (CO2) emissions in selected roadsides in Jalingo have been assessed. Four roads were used as sample locations where the concentration of CO2 and CO were measured using an air quality meter for four weeks. The mean concentration of CO2 and CO obtained were respectively as follows: 542.25 ppm and 7.49 ppm for the roadblock, 540.05 ppm and 5.55 ppm for Hammaruwa way, 598.81 ppm and 17.42 ppm for market road, and 463.80 ppm and 1.08 ppm for Nigerian Labour Congress (NLC) road (control). Based on the acceptable limit of CO2 (600 ppm), the Roadblock road, Hammaruwa way, and the NLC/control road are safe. Only the market road had value that exceeded the acceptable limit, and it may be attributed to high vehicular activities on the roadsides. Therefore, more alternative roads should be constructed in other to minimize traffic congestion and also, the use of nose masks should be encouraged. For the CO, all the sites are safe because they fall within the acceptable level of CO (1-70 ppm).

scholarly journals Comparative Analysis of Machine Learning Regression Algorithms on Air Pollution Dataset

2020 ◽  
pp. 125-136
Author(s):  
Sumit Upadhyay
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Accuracy Score ◽  
Implementation Phase ◽  
The Government ◽  
Respirable Particulate

Air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs. Examining and protecting air quality has become one of the most essential activities for the government in many industrial and urban areas today. Air pollutants, such as carbon monoxide (CO), sulfur dioxide (SO(2)), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O(3)), heavy metals, and respirable particulate matter (PM2.5 and PM10), differ in their chemical composition, reaction properties, emission, time of disintegration and ability to diffuse in long or short distances. The main objective of this paper to build a model for predicting Air Quality Index(AQI) of the specific cities using various types of machine learning algorithms namely Multiple Linear Regression, K Nearest Neighbours(KNN), Support Vector Machine(SVM) and Decision Tree. And also evaluate and compare the performance of every algorithm based on their accuracy score and errors. Air Pollution dataset is publicly available on different government sites. The implementation phase dataset is divided as 80% for the training of different models and the rest of the dataset is used for testing the model.

Sign in / Sign up

Export Citation Format

Share Document