Diurnal and Seasonal Variations of Carbon Dioxide (CO2) Concentration in Urban, Suburban, and Rural Areas around Tokyo

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

Dust Aerosol Detection by the Modified CO₂ Slicing Method

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

Latitudinal distribution of the deuterium to hydrogen ratio in the atmospheric water vapor retrieved from IMG/ADEOS data

International audienc

Sub-Daily Natural CO2 Flux Simulation Based on Satellite Data: Diurnal and Seasonal Pattern Comparisons to Anthropogenic CO2 Emissions in the Greater Tokyo Area

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

Mapping Brick Kilns to Support Environmental Impact Studies around Delhi Using Sentinel-2

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