scholarly journals Quantifying burning efficiency in Megacities using NO<sub>2</sub> / CO ratio from the Tropospheric Monitoring Instrument (TROPOMI)

2019 ◽  
Author(s):  
Srijana Lama ◽  
Sander Houweling ◽  
K. Folkert Boersma ◽  
Ilse Aben ◽  
Hugo A. C. Denier van der Gon ◽  
...  
Keyword(s):  
Los Angeles ◽  
Mexico City ◽  
Quality Monitoring ◽  
Emission Inventories ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Local Enhancement ◽  
Emission Ratio ◽  
Rotation Methods ◽  
Emission Ratios

Abstract. This study investigates the use of co-located NO2 and CO retrievals from the TROPOMI satellite to improve the quantification of burning efficiency and emission factors over the mega-cities of Tehran, Mexico City, Cairo, Riyadh, Lahore and Los Angeles. Local enhancement of CO and NO2 above megacities are well captured by TROPOMI at relatively short averaging times. In this study, the Upwind Background and Plume rotation methods are used to investigate the accuracy of satellite derived ∆NO2 / ∆CO ratios. The column enhancement ratios derived using these two methods vary by 5 to 30 % across the selected megacities. TROPOMI derived column enhancement ratios are compared with emission ratios from the EDGAR v4.3.2 and MACCity, 2018 emission inventories. TROPOMI correlates strongly (r = 0.85 and 0.7) with EDGAR and MACCity showing the highest emission ratio for Riyadh and lowest for Lahore. However, inventory derived emission ratios are higher by 60 to 80 % compared to TROPOMI column enhancement ratios across the six megacities. The short lifetime of NO2 and different vertical sensitivity of TROPOMI NO2 and CO explain most of this difference. We present a method to translate TROPOMI retrieved column enhancement ratios into corresponding emission ratio, accounting for these influences. Except for Los Angeles, TROPOMI derived emission ratios are close (within 10 to 25 %) to MACCity. For EDGAR, however, emission ratios are higher by ~80 % for Cairo, 30 to 45 % for Riyadh and ~70 % for Los Angeles. The air quality monitoring networks in Los Angeles and Mexico City are used to validate the use of TROPOMI. Over Mexico City, these measurements are consistent with TROPOMI, EDGAR and MACCIty derived emission ratios. For Los Angeles, however, EDGAR and MACCity are higher by a factor 5 compared to TROPOMI. The ground-based measurements are consistent with a poorer burning efficiency in Los Angeles as inferred from TROPOMI, demonstrating its potential to monitor burning efficiency.

scholarly journals Quantifying burning efficiency in megacities using the NO<sub>2</sub>∕CO ratio from the Tropospheric Monitoring Instrument (TROPOMI)

2020 ◽  
Vol 20 (17) ◽  
pp. 10295-10310 ◽  
Author(s):  
Srijana Lama ◽  
Sander Houweling ◽  
K. Folkert Boersma ◽  
Henk Eskes ◽  
Ilse Aben ◽  
...  
Keyword(s):  
Los Angeles ◽  
Mexico City ◽  
Atmospheric Chemistry ◽  
Combustion Efficiency ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Emission Ratio ◽  
Efficient Combustion ◽  
Rotation Methods ◽  
Emission Ratios

Abstract. This study investigates the use of co-located nitrogen dioxide (NO2) and carbon monoxide (CO) retrievals from the TROPOMI satellite to improve the quantification of burning efficiency and emission factors (EFs) over the megacities of Tehran, Mexico City, Cairo, Riyadh, Lahore, and Los Angeles. Efficient combustion is characterized by high NOx (NO+NO2) and low CO emissions, making the NO2∕CO ratio a useful proxy for combustion efficiency (CE). The local enhancement of CO and NO2 above megacities is well captured by TROPOMI at short averaging times compared with previous satellite missions. In this study, the upwind background and plume rotation methods are used to investigate the accuracy of satellite-derived ΔNO2∕ΔCO ratios. The column enhancement ratios derived using these two methods vary by 5 % to 20 % across the selected megacities. TROPOMI-derived column enhancement ratios are compared with emission ratios from the EDGAR v4.3.2 (Emission Database for Global Atmospheric Research v4.3.2) and the MACCity (Monitoring Atmospheric Chemistry and Climate and CityZen) 2018 emission inventories. TROPOMI correlates strongly (r=0.85 and 0.7) with EDGAR and MACCity, showing the highest emission ratio for Riyadh and lowest emission ratio for Lahore. However, inventory-derived emission ratios are 60 % to 85 % higher than TROPOMI column enhancement ratios across the six megacities. The short lifetime of NO2 and the different vertical sensitivity of TROPOMI NO2 and CO explain most of this difference. We present a method to translate TROPOMI-retrieved column enhancement ratios into corresponding emission ratios, thereby accounting for these influences. Except for Los Angeles and Lahore, TROPOMI-derived emission ratios are close (within 10 % to 25 %) to MACCity values. For EDGAR, however, emission ratios are ∼65 % higher for Cairo and 35 % higher for Riyadh. For Los Angeles, EDGAR and MACCity are a factor of 2 and 3 higher than TROPOMI respectively. The air quality monitoring networks in Los Angeles and Mexico City are used to validate the use of TROPOMI. For Mexico City and Los Angeles, these measurements are consistent with TROPOMI-derived emission ratios, demonstrating the potential of TROPOMI with respect to monitoring burning efficiency.

scholarly journals Analysis and optimal design of air quality monitoring networks using a variational approach

Tellus B ◽  
2015 ◽  
Vol 67 (1) ◽  
pp. 25385 ◽  
Author(s):  
Adolfo Henriquez ◽  
Axel Osses ◽  
Laura Gallardo ◽  
Melisa Diaz Resquin
Keyword(s):  
Air Quality ◽  
Optimal Design ◽  
Variational Approach ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks

Analysis of air quality monitoring networks by functional clustering

2008 ◽  
Vol 19 (7) ◽  
pp. 672-686 ◽  
Author(s):  
R. Ignaccolo ◽  
S. Ghigo ◽  
E. Giovenali
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Functional Clustering

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;

A methodology for the quantification of the net African dust load in air quality monitoring networks

2007 ◽  
Vol 41 (26) ◽  
pp. 5516-5524 ◽  
Author(s):  
M. Escudero ◽  
X. Querol ◽  
J. Pey ◽  
A. Alastuey ◽  
N. Pérez ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
African Dust ◽  
Dust Load
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