Lok N. Lamsal

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

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 worlds tropical/subtropical megacities will remain a priority, as their populations and emissions of pollutants and greenhouse gases are expected to increase significantly.

Creating National Air Pollution Models for Population Exposure Assessment in Canada

Background: Population exposure assessment methods that capture local-scale pollutant variability are needed for large-scale epidemiological studies and surveillance, policy, and regulatory purposes. Currently, such exposure methods are limited. Methods: We created 2006 national pollutant models for fine particulate matter [PM with aerodynamic diameter ≤ 2.5 μm (PM2.5)], nitrogen dioxide (NO2), benzene, ethylbenzene, and 1,3-butadiene from routinely collected fixed-site monitoring data in Canada. In multiple regression models, we incorporated satellite estimates and geographic predictor variables to capture background and regional pollutant variation and used deterministic gradients to capture local-scale variation. The national NO2 and benzene models are evaluated with independent measurements from previous land use regression models that were conducted in seven Canadian cities. National models are applied to census block-face points, each of which represents the location of approximately 89 individuals, to produce estimates of population exposure. Results: The national NO2 model explained 73% of the variability in fixed-site monitor concentrations, PM2.5 46%, benzene 62%, ethylbenzene 67%, and 1,3-butadiene 68%. The NO2 model predicted, on average, 43% of the within-city variability in the independent NO2 data compared with 18% when using inverse distance weighting of fixed-site monitoring data. Benzene models performed poorly in predicting within-city benzene variability. Based on our national models, we estimated Canadian ambient annual average population-weighted exposures (in micrograms per cubic meter) of 8.39 for PM2.5, 23.37 for NO2, 1.04 for benzene, 0.63 for ethylbenzene, and 0.09 for 1,3-butadiene. Conclusions: The national pollutant models created here improve exposure assessment compared with traditional monitor-based approaches by capturing both regional and local-scale pollution variation. Applying national models to routinely collected population location data can extend land use modeling techniques to population exposure assessment and to informing surveillance, policy, and regulation.

Scaling Relationship for NO2 Pollution and Urban Population Size: A Satellite Perspective

Concern is growing about the effects of urbanization on air pollution and health. Nitrogen dioxide (NO2) released primarily from combustion processes, such as traffic, is a short-lived atmospheric pollutant that serves as an air-quality indicator and is itself a health concern. We derive a global distribution of ground-level NO2 concentrations from tropospheric NO2 columns retrieved from the Ozone Monitoring Instrument (OMI). Local scaling factors from a three-dimensional chemistry-transport model (GEOS-Chem) are used to relate the OMI NO2 columns to ground-level concentrations. The OMI-derived surface NO2 data are significantly correlated (r = 0.69) with in situ surface measurements. We examine how the OMI-derived ground-level NO2 concentrations, OMI NO2 columns, and bottom-up NOx emission inventories relate to urban population. Emission hot spots, such as power plants, are excluded to focus on urban relationships. The correlation of surface NO2 with population is significant for the three countries and one continent examined here: United States (r = 0.71), Europe (r = 0.67), China (r = 0.69), and India (r = 0.59). Urban NO2 pollution, like other urban properties, is a power law scaling function of the population size: NO2 concentration increases proportional to population raised to an exponent. The value of the exponent varies by region from 0.36 for India to 0.66 for China, reflecting regional differences in industrial development and per capita emissions. It has been generally established that energy efficiency increases and, therefore, per capita NOx emissions decrease with urban population; here, we show how outdoor ambient NO2 concentrations depend upon urban population in different global regions.

Satellite-based Estimates of Ambient Air Pollution and Global Variations in Childhood Asthma Prevalence

Background: The effect of ambient air pollution on global variations and trends in asthma prevalence is unclear. Objectives: Our goal was to investigate community-level associations between asthma prevalence data from the International Study of Asthma and Allergies in Childhood (ISAAC) and satellite-based estimates of particulate matter with aerodynamic diameter < 2.5 µm (PM2.5) and nitrogen dioxide (NO2), and modelled estimates of ozone. Methods: We assigned satellite-based estimates of PM2.5 and NO2 at a spatial resolution of 0.1° × 0.1° and modeled estimates of ozone at a resolution of 1° × 1° to 183 ISAAC centers. We used center-level prevalence of severe asthma as the outcome and multilevel models to adjust for gross national income (GNI) and center- and country-level sex, climate, and population density. We examined associations (adjusting for GNI) between air pollution and asthma prevalence over time in centers with data from ISAAC Phase One (mid-1900s) and Phase Three (2001–2003). Results: For the 13- to 14-year age group (128 centers in 28 countries), the estimated average within-country change in center-level asthma prevalence per 100 children per 10% increase in center-level PM2.5 and NO2 was –0.043 [95% confidence interval (CI): –0.139, 0.053] and 0.017 (95% CI: –0.030, 0.064) respectively. For ozone the estimated change in prevalence per parts per billion by volume was –0.116 (95% CI: –0.234, 0.001). Equivalent results for the 6- to 7-year age group (83 centers in 20 countries), though slightly different, were not significantly positive. For the 13- to 14-year age group, change in center-level asthma prevalence over time per 100 children per 10% increase in PM2.5 from Phase One to Phase Three was –0.139 (95% CI: –0.347, 0.068). The corresponding association with ozone (per ppbV) was –0.171 (95% CI: –0.275, –0.067). Conclusion: In contrast to reports from within-community studies of individuals exposed to traffic pollution, we did not find evidence of a positive association between ambient air pollution and asthma prevalence as measured at the community level.

Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study

BackgroundFew epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.MethodsNational spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2) and a chemical transport model (for O3). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.ResultsCalibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38) and O3 (R2 = 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 μg/m3 (SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM2.5, NO2 and O3 exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.ConclusionsWe demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.For submission to: Environmental Health

Trends and variability in surface ozone over the United States

We investigate the observed trends and interannual variability in surface ozone over the United States using the Global Modeling Initiative chemical transport model. We discuss the roles of meteorology, emissions, and transport from the stratosphere in driving the interannual variability in different regions and seasons. We demonstrate that a hindcast simulation for 1991–2010 can reproduce much of the observed variability and the trends in summertime ozone, with correlation coefficients for seasonally and regionally averaged median ozone ranging from 0.46 to 0.89. Reproducing the interannual variability in winter and spring in the western United States may require higher-resolution models to adequately represent stratosphere-troposphere exchange. Hindcast simulations with fixed versus variable emissions show that changes in anthropogenic emissions drive the observed negative trends in monthly median ozone concentrations in the eastern United States during summer, as well as the observed reduction in the amplitude of the seasonal cycle. The simulation underestimates positive trends in the western United States during spring, but excluding the first 4 years of data removes many of the statistically significant trends in this region. The reduction in the slope of the ozone versus temperature relationship before and after major emission reductions is also well represented by the model. Our results indicate that a global model can reproduce many of the important features of the meteorologically induced ozone variability as well as the emission-driven trends, lending confidence to model projections of future changes in regional surface ozone.

Global dry deposition of nitrogen dioxide and sulfur dioxide inferred from space‐based measurements

A method is developed to estimate global NO2 and SO2 dry deposition fluxes at high spatial resolution (0.1°×0.1°) using satellite measurements from the Ozone Monitoring Instrument (OMI) on the Aura satellite, in combination with simulations from the Goddard Earth Observing System chemical transport model (GEOS-Chem). These global maps for 2005–2007 provide a data set for use in examining global and regional budgets of deposition. In order to properly assess SO2 on a global scale, a method is developed to account for the geospatial character of background offsets in retrieved satellite columns. Globally, annual dry deposition to land estimated from OMI as NO2 contributes 1.5 ± 0.5 Tg of nitrogen and as SO2 contributes 13.7 ± 4.0 Tg of sulfur. Differences between OMI-inferred NO2 dry deposition fluxes and those of other models and observations vary from excellent agreement to an order of magnitude difference, with OMI typically on the low end of estimates. SO2 dry deposition fluxes compare well with in situ Clear Air Status and Trends Network-inferred flux over North America (slope = 0.98, r = 0.71). The most significant NO2 dry deposition flux to land per area occurs in the Pearl River Delta, China, at 13.9 kg N ha−1 yr−1, while SO2 dry deposition has a global maximum rate of 72.0 kg S ha−1 yr−1 to the east of Jinan in Chinas Shandong province. Dry deposition fluxes are explored in several urban areas, where NO2 contributes on average 9–36% and as much as 85% of total NOy dry deposition.

Revising the slant column density retrieval of nitrogen dioxide observed by the Ozone Monitoring Instrument

Abstract Nitrogen dioxide retrievals from the Aura/Ozone Monitoring Instrument (OMI) have been used extensively over the past decade, particularly in the study of tropospheric air quality. Recent comparisons of OMI NO2 with independent data sets and models suggested that the OMI values of slant column density (SCD) and stratospheric vertical column density (VCD) in both the NASA OMNO2 and Royal Netherlands Meteorological Institute DOMINO products are too large, by around 10–40%. We describe a substantially revised spectral fitting algorithm, optimized for the OMI visible light spectrometer channel. The most important changes comprise a flexible adjustment of the instrumental wavelength shifts combined with iterative removal of the ring spectral features; the multistep removal of instrumental noise; iterative, sequential estimates of SCDs of the trace gases in the 402–465 nm range. These changes reduce OMI SCD(NO2) by 10–35%, bringing them much closer to SCDs retrieved from independent measurements and models. The revised SCDs, submitted to the stratosphere‐troposphere separation algorithm, give tropospheric VCDs ∼10–15% smaller in polluted regions, and up to ∼30% smaller in unpolluted areas. Although the revised algorithm has been optimized specifically for the OMI NO2 retrieval, our approach could be more broadly applicable.

Satellite Ozone Retrieval Under Broken Cloud Conditions: An Error Analysis Based on Monte Carlo Simulations

This paper investigates the influence of horizontally inhomogeneous clouds on the accuracy of total ozone column retrievals from space. The focus here is on retrievals based on backscattered ultraviolet light measurements in Huggins bands in the range of 315-340 nm. It is found that simplifying the description of cloud properties in the ozone-retrieval algorithm studied can produce errors of up to 6%, depending on the error in the assumed cloud parameters. Yet another finding is the fact that independent pixel approximation suffices for ozone-retrieval algorithms. This was found using three-dimensional Monte Carlo radiative transfer calculations in the Huggins bands

A Satellite-Based Multi-Pollutant Index of Global Air Quality

ir pollution is a major health hazard that is responsible formillions of annual excess deaths worldwide. Simpleindicators are useful for comparative studies and to assesstrends over time. The development of global indicators hasbeen impeded by the lack of ground-based observations in vastregions of the world. Recognition is growing of the need for amultipollutant approach to air quality to better representhuman exposure. Here we introduce the prospect of amultipollutant air quality indicator based on observationsfrom satellite remote sensing.A variety of air pollution indices exist around the world. Theair quality index of the U.S. Environmental Protection Agencyat a given time and place is based on the highest concentrationrelative to the ambient air quality standard for PM