A space‐based, high‐resolution view of notable changes in urban NO_x pollution around the world (2005–2014)

Abstract

Nitrogen oxides (NOx = NO + NO2) are produced during combustion processes and, thus may serve as a proxy for fossil fuel‐based energy usage and coemitted greenhouse gases and other pollutants. We use high‐resolution nitrogen dioxide (NO2) data from the Ozone Monitoring Instrument (OMI) to analyze changes in urban NO2 levels around the world from 2005 to 2014, finding complex heterogeneity in the changes. We discuss several potential factors that seem to determine these NOx changes. First, environmental regulations resulted in large decreases. The only large increases in the United States may be associated with three areas of intensive energy activity. Second, elevated NO2 levels were observed over many Asian, tropical, and subtropical cities that experienced rapid economic growth. Two of the largest increases occurred over recently expanded petrochemical complexes in Jamnagar (India) and Daesan (Korea). Third, pollution transport from China possibly influenced the Republic of Korea and Japan, diminishing the impact of local pollution controls. However, in China, there were large decreases over Beijing, Shanghai, and the Pearl River Delta, which were likely associated with local emission control efforts. Fourth, civil unrest and its effect on energy usage may have resulted in lower NO2 levels in Libya, Iraq, and Syria. Fifth, spatial heterogeneity within several megacities may reflect mixed efforts to cope with air quality degradation. We also show the potential of high‐resolution data for identifying NOx emission sources in regions with a complex mix of sources. Intensive monitoring of the world's tropical/subtropical megacities will remain a priority, as their populations and emissions of pollutants and greenhouse gases are expected to increase significantly.

The Dawn of Geostationary Air Quality Monitoring: Case Studies From Seoul and Los Angeles

“…Here, the OMPS NM average measurements show that NO 2 columns are locally maximum over the SMA and the LA Basin in each respective region. By providing the means to distinguish sources, long-term trends can be used to evaluate the changes of emissions driven by regulatory programs (Kim et al, 2006), technological controls (e.g., Russell et al, 2012), and economic activity (e.g., Russell et al, 2012;de Foy et al, 2016;Duncan et al, 2016). Whether considering daily measurements or analysis of long term monthly averages, instruments like OMPS NM provide a well-characterized, quantitatively stable measurement reflecting a balance of NO 2 emissions and removal at spatial scales of ∼25 km, with some limited information on pollutant transport (e.g., Beirle et al, 2011;Valin et al, 2013Valin et al, , 2014de Foy et al, 2016).…”

“…These data have been useful for understanding global (e.g., Martin et al, 2003;Jaegl et al, 2005), regional (e.g., Duncan et al, 2016;Travis et al, 2016) and local air quality (e.g., Zhu et al, 2017) over daily (e.g., Valin et al, 2014;de Foy et al, 2016), seasonal (e.g., Russell et al, 2010), interannual, and decadal time periods (van der et al, 2008;De Smedt et al, 2015). However, the relatively coarse spatial resolutions and single daily observation times have substantially limited these applications, particularly within the air quality management community which needs to be able to distinguish temporal profiles of emissions from different source sectors and identify specific physical processes to justify regulatory decisions.…”

Improving PM2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework

“…Consistent with previous studies, our analysis also shows that the percentage improvements by the CART-LM-KF-AN method are generally larger in relatively cleaner regions (e.g., the Pearl River Delta in South china, Northeast China, and other remote regions) than in heavily polluted regions (e.g., the North China Plain and the Yangtze River Delta in East China) (Figure 8), suggesting that there might be important factors missing in the trained relationship between model biases and predictor variables over polluted regions. One such factor is the fast-changing emissions in both magnitude and distribution in regions such as the North China Plain and the Yangtze River Delta during the modeled three years [48][49][50], a result of increasingly more strict emission control enforcements and/or economic fluctuations. The significant change of emission rates in these regions between the training years (2014)(2015) and the prediction year (2016) could confound the trained bias correction relationships.…”

Urban Emissions and Regional Air Quality in India

“…Many studies on Indian air quality rely on satellite observations as a result of these biases and limited availability of ground-based monitor data across India, including NO 2 from OMI aboard the Aura satellite [Lamsal et al, 2010;Ghude et al, 2013]. Satellite observations from OMI and other instruments have been previously used to evaluate emissions and surface concentrations [Lamsal et al, 2010;Lu and Streets, 2012;Lu et al, 2013;Streets et al, 2013], observe trends in air quality [Lamsal et al, 2013[Lamsal et al, , 2015Duncan et al, 2015;Krotkov et al, 2015], evaluate AOD for dust or anthropogenic pollution [King et al, 2003;Isakov et al, 2007;Zhao et al, 2010], and estimate NO X to VOC ratios in assessing O 3 regimes [Jin and Holloway, 2015]. Limitations of satellite observations include temporal availability (i.e.…”