COVID-19 related restrictions lowered particulate matter and trace gas concentrations across cities around the world, providing a natural opportunity to study effects of anthropogenic activities on ...emissions of air pollutants. In this paper, the impact of sudden suspension of human activities on air pollution was analyzed by studying the change in satellite retrieved NO
concentrations and top-down NOx emission over the urban and rural areas around Delhi. NO
was chosen for being the most indicative of emission intensity due to its short lifetime of the order of a few hours in the planetary boundary layer. We present a robust temporal comparison of Ozone Monitoring Instrument (OMI) retrieved NO
column density during the lockdown with the counterfactual baseline concentrations, extrapolated from the long-term trend and seasonal cycle components of NO
using observations during 2015 to 2019. NO
concentration in the urban area of Delhi experienced an anomalous relative change ranging from 60.0% decline during the Phase 1 of lockdown (March 25-April 13, 2020) to 3.4% during the post-lockdown Phase 5. In contrast, we find no substantial reduction in NO
concentrations over the rural areas. To segregate the impact of the lockdown from the meteorology, weekly top-down NOx emissions were estimated from high-resolution TROPOspheric Monitoring Instrument (TROPOMI) retrieved NO
by accounting for horizontal advection derived from the steady state continuity equation. NOx emissions from urban Delhi and power plants exhibited a mean decline of 72.2% and 53.4% respectively in Phase 1 compared to the pre-lockdown business-as-usual phase. Emission estimates over urban areas and power-plants showed a good correlation with activity reports, suggesting the applicability of this approach for studying emission changes. A higher anomaly in emission estimates suggests that comparison of only concentration change, without accounting for the dynamical and photochemical conditions, may mislead evaluation of lockdown impact. Our results shall also have a broader impact for optimizing bottom-up emission inventories.
Site environments and instrumental characteristics of carbon dioxide (CO2) measurements operated by local governments in the Kanto Plain, the center of which is Tokyo, were summarized for this study. ...The observation sites were classified into environments of three types: urban, suburban, and woodland. Based on a few decades of accumulated hourly data, the diurnal and seasonal variations of CO2 concentrations were analyzed as a composite of anomalies from annual means recorded for each site. In urban areas, the highest concentrations appear before midnight in winter. The second peak corresponds to the morning rush hour and the strengthening of the inversion layer. Suburban areas can be characterized as having the highest concentration before dawn and the lowest concentration during the daytime in summer in association with the activation of respiration and photosynthesis of vegetation. In these areas, concentration peaks also appear during the morning rush hour. Woodland areas show background features, with the highest concentration in early spring, which are higher than the global background by about 5 ppmv.
Hyperspectral thermal infrared sounders enable us to grasp the global behavior of minor atmospheric constituents. Ammonia, which imparts large impacts on the atmospheric environment by reacting with ...other species, is one of them. In this work, we present an ammonia retrieval system that we developed for the Greenhouse Gases Observing Satellite (GOSAT) and the estimates of global atmospheric ammonia column amounts that we derived from 2009 to 2014. The horizontal distributions of the seasonal ammonia column amounts represent significantly high values stemming from six anthropogenic emission source areas and four biomass burning ones. The monthly mean time series of these sites were investigated, and their seasonality was clearly revealed. A comparison with the Infrared Atmospheric Sounding Interferometer (IASI) ammonia product showed good agreement spatially and seasonally, though there are some differences in detail. The values from GOSAT tend to be slightly larger than those from IASI for low concentrations, especially in spring and summer. On the other hand, they are lower for particularly high concentrations during summer, such as eastern China and northern India. In addition, the largest differences were observed in central Africa. These differences seem to stem from the temporal gaps in observations and the fundamental differences in the retrieval systems.
Dust aerosols, which have diverse and strong influences on the environment, must be monitored. Satellite data are effective for monitoring atmospheric conditions globally. In this work, the modified ...CO₂ slicing method, a cloud detection technique using thermal infrared data from space, was applied to GOSAT data to detect the dust aerosol layer height. The results were compared using lidar measurements. Comparison of horizontal distributions found for northern Africa during summer revealed that both the relative frequencies of the low level aerosol layer from the slicing method and the dust frequencies of CALIPSO are high in northern coastal areas. Comparisons of detected layer top heights using collocated data with CALIPSO and ground-based lidar consistently showed high detection frequencies of the lower level aerosol layer, although the slicing method sometimes produces overestimates. This tendency is significant over land. The main causes of this tendency might be uncertainty of the surface skin temperature and a temperature inversion layer in the atmosphere. The results revealed that obtaining the detailed behavior of dust aerosols using the modified slicing method alone is difficult.
The Greenhouse Gases Observing Satellite (GOSAT) was successfully launched in January 2009, with the aim of providing global observations of greenhouse gases. We developed an algorithm to retrieve ...CO2 vertical profiles from the terrestrial radiation spectra at 700–800 cm−1 and assessed its validity. For this purpose, we first computed GOSAT pseudomeasurement spectra and then performed CO2 retrieval simulations using the maximum a posteriori (MAP) method, with analytical data for temperature information. Our simulations with no uncertainty in the estimates of atmospheric conditions such as surface temperature, surface emissivity, and profiles of temperature, water vapor, and ozone showed that the retrieved CO2 profiles had an accuracy of 1% above 800 hPa, with little dependence on the a priori profiles. Introducing correlations between layers in an a priori error covariance matrix was important for CO2 retrieval especially above 200 hPa. Enhancing the correlations below 800 hPa was important for CO2 retrieval there. Selecting 100 channels based on CO2 information content for all layers, 10 channels for the region above 55 hPa, and 50 channels for the region below 800 hPa was sufficient to achieve CO2 retrieval with 1% accuracy from the troposphere through the stratosphere. Our simulations with possible errors in the atmospheric conditions showed that 1% accuracy was also achieved at 600–100 hPa in every latitude region, although the retrieved CO2 concentrations probably included up to 4% positive and negative biases at 30°S–30°N above 100 hPa and at mid‐ and high latitudes below 600 hPa, respectively.
CO2 concentrations in the upper troposphere were retrieved from thermal infrared spectra as observed by the only spaceborne hyperspectral sounder launched in the 1990s: the Interferometric Monitor ...for Greenhouse gases (IMG) onboard the Advanced Earth Observing Satellite (ADEOS). First, the effective optical path difference of the IMG was evaluated because the actual instrumental line shape function of the interferometer component has not been evaluated for technical reasons in the orbit. The CO2 retrieval method was based on the maximum a posteriori (MAP) retrieval method and on procedures to decrease errors that obstruct CO2 signal detection. For the retrieval analysis, ERA-40 re-analysis meteorological data were used as temperature field data. A method of selecting effective channels for CO2 retrieval was used to remove channels with a high temperature dependency and to reduce errors in estimating the water vapor, ozone, and surface temperature. Furthermore, uncertainties in temperature and other error factors, which cannot be removed through channel selection, were evaluated and optimized by treating them as components of measurement errors in the MAP retrieval. CO2 retrieval noises of the MAP retrieval were estimated as 2.5 % and 2.0 % at pressure levels of 500 and 300 hPa, respectively. CO2 concentrations retrieved from IMG data were compared with aircraft measurement data. Results showed that the random error in the IMG retrieval was smaller than that estimated as the a posteriori error of the MAP retrieval. No significant biases were shown compared with the margin of random errors. The CO2 retrieval method was applied to IMG data measured in April, 1997. Although assuming a uniform CO2 concentration as a priori, the latitudinal gradient of the zonal mean concentration was consistent with climatological features presented by previous studies at pressure levels of 500 and 300 hPa. These results suggest that thermal infrared observation by the IMG is effective for evaluating the upper tropospheric CO2 concentration in the 1990s.
Several studies have found rising ambient particulate matter (PM 2.5 ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which ...is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM 2.5 ambient concentrations and assessing whether urban expansion can explain rise of PM 2.5 ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change’s (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM 2.5 concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM 2.5 emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory.
During the last decade, advances in the remote sensing of greenhouse gas (GHG) concentrations by the Greenhouse Gases Observing SATellite-1 (GOSAT-1), GOSAT-2, and Orbiting Carbon Observatory-2 ...(OCO-2) have produced finer-resolution atmospheric carbon dioxide (CO2) datasets. These data are applicable for a top-down approach towards the verification of anthropogenic CO2 emissions from megacities and updating of the inventory. However, great uncertainties regarding natural CO2 flux estimates remain when back-casting CO2 emissions from concentration data, making accurate disaggregation of urban CO2 sources difficult. For this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) land products, meso-scale meteorological data, SoilGrids250 m soil profile data, and sub-daily soil moisture datasets to calculate hourly photosynthetic CO2 uptake and biogenic CO2 emissions with 500 m resolution for the Kantō Plain, Japan, at the center of which is the Tokyo metropolis. Our hourly integrated modeling results obtained for the period 2010–2018 suggest that, collectively, the vegetated land within the Greater Tokyo Area served as a daytime carbon sink year-round, where the hourly integrated net atmospheric CO2 removal was up to 14.15 ± 4.24% of hourly integrated anthropogenic emissions in winter and up to 55.42 ± 10.39% in summer. At night, plants and soil in the Greater Tokyo Area were natural carbon sources, with hourly integrated biogenic CO2 emissions equivalent to 2.27 ± 0.11%–4.97 ± 1.17% of the anthropogenic emissions in winter and 13.71 ± 2.44%–23.62 ± 3.13% in summer. Between January and July, the hourly integrated biogenic CO2 emissions of the Greater Tokyo Area increased sixfold, whereas the amplitude of the midday hourly integrated photosynthetic CO2 uptake was enhanced by nearly five times and could offset up to 79.04 ± 12.31% of the hourly integrated anthropogenic CO2 emissions in summer. The gridded hourly photosynthetic CO2 uptake and biogenic respiration estimates not only provide reference data for the estimation of total natural CO2 removal in our study area, but also supply prior input values for the disaggregation of anthropogenic CO2 emissions and biogenic CO2 fluxes when applying top-down approaches to update the megacity’s CO2 emissions inventory. The latter contribution allows unprecedented amounts of GOSAT and ground measurement data regarding CO2 concentration to be analyzed in inverse modeling of anthropogenic CO2 emissions from Tokyo and the Kantō Plain.
Exposure to particulate matter less than 2.5 µm in diameter (PM
) is a cause of concern in cities and major emission regions of northern India. An intensive field campaign involving the states of ...Punjab, Haryana and Delhi national capital region (NCR) was conducted in 2022 using 29 Compact and Useful PM
Instrument with Gas sensors (CUPI-Gs). Continuous observations show that the PM
in the region increased gradually from < 60 µg m
in 6-10 October to up to 500 µg m
on 5-9 November, which subsequently decreased to about 100 µg m
in 20-30 November. Two distinct plumes of PM
over 500 µg m
are tracked from crop residue burning in Punjab to Delhi NCR on 2-3 November and 10-11 November with delays of 1 and 3 days, respectively. Experimental campaign demonstrates the advantages of source region observations to link agricultural waste burning and air pollution at local to regional scales.
Cities lying in the Indo-Gangetic plains of South Asia have the world’s worst anthropogenic air pollution, which is often attributed to urban growth. Brick kilns, facilities for producing fired ...clay-bricks for construction are often found at peri-urban region of South Asian cities. Although brick kilns are significant air pollutant emitters, their contribution in under-represented in air pollution emission inventories due to unavailability of their distribution. This research overcomes this gap by proposing publicly available remote sensing dataset based approach for mapping brick-kiln locations using object detection and pixel classification. As brick kiln locations are not permanent, an open-dataset based methodology is advantageous for periodically updating their locations. Brick kilns similar to Bull Trench Kilns were identified using the Sentinel-2 imagery around the state of Delhi in India. The unique geometric and spectral features of brick kilns distinguish them from other classes such as built-up, vegetation and fallow-land even in coarse resolution imagery. For object detection, transfer learning was used to overcome the requirement of huge training datasets, while for pixel-classification random forest algorithm was used. The method achieved a recall of 0.72, precision of 0.99 and F1 score of 0.83. Overall 1564 kilns were detected, which are substantially higher than what was reported in an earlier study over the same region. We find that brick kilns are located outside urban areas in proximity to outwardly expanding built-up areas and tall built structures. Duration of brick kiln operation was also estimated by analyzing the time-series of normalized difference vegetation index (NDVI) over the brick kiln locations. The brick kiln locations can be further used for updating land-use emission inventories to assess particulate matter and black carbon emissions.
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