Evaluation of TROPOMI/Sentinel-5 Precursor NO2 product against ground-based observations in Helsinki and first applications to Finnish society

2021 ◽  
Author(s):  
Iolanda Ialongo ◽  
Henrik Virta ◽  
Henk Eskes ◽  
Jari Hovila ◽  
John Douros ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Transport Model ◽  
A Priori ◽  
Pollution Assessment ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Chemical Transport Model ◽  
Weekly Cycle ◽  
Absolute Bias

<p>We evaluate the satellite-based TROPOMI (TROPOspheric Monitoring Instrument) NO2 products against ground-based observations in Helsinki (Finland). TROPOMI NO2 total (summed) columns are compared with the measurements performed by the Pandora spectrometer during April–September 2018. The mean relative and absolute bias between the TROPOMI and Pandora NO2 total columns is about 10% and 0.12 × 10<sup>15</sup> molec. cm<sup>-2</sup> respectively.<span> </span></p><p>We find high correlation (r = 0.68) between satellite- and ground-based data, but also that TROPOMI total columns underestimate ground-based observations for relatively large Pandora NO2 total columns, corresponding to episodes of relatively elevated pollution. This is expected because of the relatively large size of the TROPOMI ground pixel (3.5 × 7 km) and the a priori used in the retrieval compared to the relatively small field-of-view of the Pandora instrument. On the other hand, TROPOMI slightly overestimates relatively small NO2 total columns. Replacing the coarse a priori NO2 profiles with high-resolution profiles from the CAMS chemical transport model improves the agreement between TROPOMI and Pandora total columns for episodes of NO2 enhancement, but the overall bias remains the same (within the uncertainties).</p><p>In order to evaluate the capability of TROPOMI observations for monitoring urban air quality, we also analyse the consistency between satellite-based data and NO2 surface concentrations from the Kumpula air quality station in Helsinki. We find similar day-to-day variability between TROPOMI and in situ measurements, with NO2 enhancements observed during the same days. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities (as expected in urban sites).</p><p>Several applications have been already carried on to support informed decision making and Finnish society in general. We developed a simple web platform to inform environmental authorities at municipal level about the use of satellite observations for air quality monitoring. We assisted the Finnish authorities during the first period of the COVID-19 pandemic in assessing the effect of the lockdown on air quality. We supported the Finnish Ministry of Environment in compiling the periodic national air pollution assessment report to the EU. We participated in several international cooperation projects for assessing the major air pollution sources and the available air quality monitoring systems over several developing countries and for providing recommendations on strengthening air quality monitoring. We collaborated with the department of Social Science at the Univ. of Helsinki for the assessment of the environmental impacts of the energy and extracting sector in Yakutia (Russia).</p><p>Reference: Ialongo, I., Virta, H., Eskes, H., Hovila, J., and Douros, J.: Comparison of TROPOMI/Sentinel-5 Precursor NO<sub>2</sub> observations with ground-based measurements in Helsinki, Atmos. Meas. Tech., 13, 205–218, https://doi.org/10.5194/amt-13-205-2020, 2020.</p>

Comparison of TROPOMI/Sentinel-5 Precursor NO2 product with ground-based observations in Helsinki and first societal applications

2020 ◽  
Author(s):  
Iolanda Ialongo ◽  
Henrik Virta ◽  
Henk Eskes ◽  
Jari Hovila ◽  
John Douros
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
A Priori ◽  
Urban Air Quality ◽  
Oil Refinery ◽  
Urban Air ◽  
Chemical Transport Model ◽  
Weekly Cycle ◽  
Oil Refineries

<p>We evaluate the satellite-based TROPOMI (TROPOspheric Monitoring Instrument) NO2 products against ground-based observations in Helsinki (Finland). TROPOMI NO2 total (summed) columns are compared with the measurements performed by the Pandora spectrometer during April–September 2018. The mean relative and absolute bias between the TROPOMI and Pandora NO2 total columns is about 10 % and 0.12 × 10<sup>15</sup> molec. cm<sup>-2</sup> respectively.<span> </span></p><p>We find high correlation (r = 0.68) between satellite- and ground-based data, but also that TROPOMI total columns underestimate ground-based observations for relatively large Pandora NO2 total columns, corresponding to episodes of relatively elevated pollution. This is expected because of the relatively large size of the TROPOMI ground pixel (3.5 × 7 km) and the a priori used in the retrieval compared to the relatively small field-of-view of the Pandora instrument. On the other hand, TROPOMI slightly overestimates relatively small NO2 total columns. Replacing the coarse a priori NO2 profiles with high-resolution profiles from the CAMS chemical transport model improves the agreement between TROPOMI and Pandora total columns for episodes of NO2 enhancement.<span> </span></p><p>In order to evaluate the capability of TROPOMI observation for monitoring urban air quality, we also analyse the consistency between satellite-based data and NO2 surface concentrations from the local air quality station. We find similar day-to-day variability between TROPOMI and in situ measurements, with NO2 enhancements observed during the same days. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities (as expected in urban sites). The TROPOMI NO2 maps reveal also spatial features, such as the main traffic ways, the airport and other industrial areas, as well as the effect of the prevailing south-west wind patterns.<span> </span></p><p>These first results confirm that TROPOMI NO2 products are valuable to complement the traditional ground-based in situ data for monitoring urban air quality and are already tested by local and national authorities as well as private companies to monitor pollution sources in the Helsinki region (e.g., emissions from traffic, energy production or oil refineries). For example, TROPOMI NO2 products are already used by the oil refinery company NESTE in their sustainability report and by the Finnish Ministry of Environment to map the air pollution levels in Finland.</p><p>Ialongo, I., Virta, H., Eskes, H., Hovila, J., and Douros, J.: Comparison of TROPOMI/Sentinel 5 Precursor NO2 observations with ground-based measurements in Helsinki, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-329, accepted for publication, 2020.</p>

scholarly journals Comparison of TROPOMI/Sentinel 5 Precursor NO<sub>2</sub> observations with ground-based measurements in Helsinki

2019 ◽  
Author(s):  
Iolanda Ialongo ◽  
Henrik Virta ◽  
Henk Eskes ◽  
Jari Hovila ◽  
John Douros
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
A Priori ◽  
Quality Data ◽  
High Temporal Resolution ◽  
Chemical Transport Model ◽  
Early Evaluation ◽  
Weekly Cycle ◽  
Urban Sites

Abstract. We present a comparison between satellite-based TROPOMI (TROPOspheric Monitoring Instrument) NO2 products and ground-based observations in Helsinki (Finland). TROPOMI NO2 total (summed) columns are compared with the measurements performed by the Pandora spectrometer during April–September 2018. We find a high correlation (r = 0.68) between satellite- and ground-based data, but also that TROPOMI total columns underestimate ground-based observations for relatively large Pandora NO2 total columns, corresponding to episodes of relatively elevated pollution. This is expected because of the relatively large size of the TROPOMI ground pixel (3.5 km x 7 km) and the a-priori used in the retrieval compared to the relatively small field-of-view of the Pandora instrument. Replacing the coarse a-priori NO2 profiles with high-resolution profiles from the CAMS chemical transport model improves the agreement between TROPOMI and Pandora total columns for episodes of NO2 enhancement. We also analyse the consistency between satellite-based data and in situ NO2 surface concentrations measured at the Helsinki-Kumpula air quality station (located a few metres from the Pandora spectrometer). We find similar day-to-day variability between TROPOMI, Pandora and in situ measurements, with NO2 enhancements observed during the same days. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities (as expected in urban sites). The TROPOMI NO2 maps reveal also spatial features, such as the main traffic ways and the airport area, as well as the effect of the prevailing south-west wind patterns. This is one of the first works in which TROPOMI NO2 retrievals are validated against ground-based observations and the results provide an early evaluation of their applicability for monitoring pollution levels in urban sites. Overall, TROPOMI retrievals are valuable to complement the ground-based air quality data (available with high temporal resolution) for describing the spatio-temporal variability of NO2, even in a relatively small city like Helsinki.

scholarly journals The added value of a visible channel to a geostationary thermal infrared instrument to monitor ozone for air quality

2014 ◽  
Vol 7 (2) ◽  
pp. 1645-1689
Author(s):  
E. Hache ◽  
J.-L. Attié ◽  
C. Tourneur ◽  
P. Ricaud ◽  
L. Coret ◽  
...  
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
Statistical Tests ◽  
Surface Ozone ◽  
A Priori ◽  
Thermal Infrared ◽  
Added Value ◽  
Chemical Transport Model ◽  
Geo Satellite ◽  
Sky Conditions

Abstract. Ozone is a tropospheric pollutant and plays a key role in determining the air quality that affects human wellbeing. In this study, we compare the capability of two hypothetical grating spectrometers onboard a geostationary (GEO) satellite to sense ozone in the lowermost troposphere (surface and the 0–1 km column). We consider one week during the Northern Hemisphere summer simulated by a chemical transport model, and use the two GEO instrument configurations to measure ozone concentration (1) in the thermal infrared (GEO TIR) and (2) in the thermal infrared and the visible (GEO TIR+VIS). These configurations are compared against each other, and also against an ozone reference state and a priori ozone information. In a first approximation, we assume clear sky conditions neglecting the influence of aerosols and clouds. A number of statistical tests are used to assess the performance of the two GEO configurations. We consider land and sea pixels and whether differences between the two in the performance are significant. Results show that the GEO TIR+VIS configuration provides a better representation of the ozone field both for surface ozone and the 0–1 km ozone column during the daytime especially over land.

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

An assessment of ambient air quality using AQI and exceedance factor for Udaipur City, Rajasthan (India)

2021 ◽  
pp. 94-106
Author(s):  
Porush Kumar ◽  
Kuldeep ◽  
Nilima Gautam
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pollution Control ◽  
Urban Air Pollution ◽  
Ambient Air ◽  
Quality Monitoring ◽  
Urban Air ◽  
Air Quality Monitoring ◽  
Ambient Air Quality ◽  
Control Board

Air pollution is a severe issue of concern worldwide due to its most significant environmental risk to human health today. All substances that appear in excessive amounts in the environment, such as PM10, NO2, or SO2, may be associated with severe health problems. Anthropogenic sources of these pollutants are mainly responsible for the deterioration of urban air quality. These sources include stationary point sources, mobile sources, waste disposal landfills, open burning, and similar others. Due to these pollutants, people are at increased risk of various serious diseases like breathing problems and heart disease, and the death rate due to these diseases can also increase. Hence, air quality monitoring is essential in urban areas to control and regulate the emission of these pollutants to reduce the health impacts on human beings. Udaipur has been selected for the assessment of air quality with monitored air quality data. Air quality monitoring stations in Udaipur city are operated by the CPCB (Central Pollution Control Board) and RSPCB (Rajasthan State Pollution Control Board). The purpose of this study is to characterize the level of urban air pollution through the measurement of PM10, NO2, or SO2 in Udaipur city, Rajasthan (India). Four sampling locations were selected for Udaipur city to assess the effect of urban air pollution and ambient air quality, and it was monitored for a year from 1st January 2019 to 31st December 2019. The air quality index has been calculated with measured values of PM10, NO2, and SO2. The concentration of PM10 is at a critical level of pollution and primarily responsible for bad air quality and high air quality Index in Udaipur city.

scholarly journals Healthier breath for all and everywhere – Urban air quality monitoring experience

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
J Gajic ◽  
D Dimovski ◽  
B Vukajlovic ◽  
M Jevtic
Keyword(s):  
Public Health ◽  
Air Pollution ◽  
Air Quality ◽  
Health Risk ◽  
Urban Health ◽  
Urban Areas ◽  
Danube River ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Residential Areas

Abstract Issue/problem Increasing attention is being paid to air pollution as one of the greatest threats to public and urban health. The WHO’s Urban Health Initiative points out the importance of collecting data and mapping the present state of air quality in urban areas. For citizens, such engagement is enabled by the appearance of personal air quality measurement devices that use crowd-sourcing to make measurement results publicly accessible in real time. Description of the problem As a way of contributing to air pollution monitoring in their town, three PhD Public health students conducted over 40 measurements between the start of June and end of August 2018 on various locations in the city of Novi Sad, Serbia. Measurements were performed using AirBeam personal air quality monitoring devices and their results presented as μg/m3 of Particulate Matter 2.5 (PM2.5) and automatically uploaded to the internet using the Air-casting app. Results Measurements conducted in public transportation vehicles returned the rather high average value of 40 μg/m3, where coffee shops and restaurants scored an even higher value of 48,67 μg/m3. The lowest average air pollution levels were registered near the Danube river bank (5.67) and in the parks (6), while the sites near crossroads or in the street showed average air pollution of 8.33 μg/m3. Residential areas where smoking is present during the day reported 2.5 times higher PM2.5 values than those without smokers (33.8 and 12.78 μg/m3). Lessons Bearing in mind that the air quality is considered as a serious health risk in urban areas, results of this pilot investigation suggest potential health risk for citizens living in urban areas. The negative effects of combustion and smoking on air quality are strongly highlighted, as well as the positive impact of green areas and parks near residential areas. Key messages Air pollution exposure as a serious health risk in urban areas. Crowdsourcing as a way of air quality monitoring has great potential for contributing to public health.

scholarly journals One-Year Simulation of Ozone and Particulate Matter in China Using WRF/CMAQ Modeling System

2016 ◽  
Author(s):  
Jianlin Hu ◽  
Jianjun Chen ◽  
Qi Ying ◽  
Hongliang Zhang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Model Performance ◽  
Monitoring Network ◽  
Organic Aerosol ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Low Concentration ◽  
Temporal And Spatial

Abstract. China has been experiencing severe air pollution in recent decades. Although ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research &amp; Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) was conducted to provide detailed temporal and spatial information of ozone (O3), PM2.5 total and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, over-prediction of O3 generally occurs at low concentration range while under-prediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in Southern China than in Northern, Central and Sichuan basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of CMAQ model in reproducing severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea

2020 ◽  
Author(s):  
Woo-Sik Jung ◽  
Woo-Gon Do
Keyword(s):  
Air Pollution ◽  
Cluster Analysis ◽  
Air Quality ◽  
Monitoring Network ◽  
Hierarchical Cluster ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Monitoring Stations

&lt;p&gt;&lt;strong&gt;With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).&lt;/strong&gt;&lt;/p&gt;

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