Regional-Scale Estimation of Electric Power and Power Plant CO2

Apr 24, 2014 - Regional-Scale Estimation of Electric Power and Power Plant CO2 Emissions Using Defense Meteorological Satellite Program Operational Li...
1 downloads 13 Views 4MB Size
Download PDF
Letter pubs.acs.org/journal/estlcu

Regional-Scale Estimation of Electric Power and Power Plant CO2 Emissions Using Defense Meteorological Satellite Program Operational Linescan System Nighttime Satellite Data Husi Letu,*,† Takashi Y. Nakajima,† and Fumihiko Nishio‡ †

Research and Information Center, Tokai University, 4-1-1 Kitakaname Hiratsuka, Kanagawa 259-1292, Japan Center of Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

‡

ABSTRACT: Estimation of electric power and power plant CO2 emissions using satellite remote sensing data is essential for the management of energy consumption and greenhouse gas monitoring. For estimation, the relationship between Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) annual nighttime stable light product (NSL) for 2006 and statistical data on power generation, power consumption, and power plant CO2 emissions in 10 electric power supply regions of Japan was investigated. Unlike other power plants, thermal plants directly emit CO2 by burning fossil fuels when generating electricity. Among the nighttime lights in the NSL, only light from thermal power is related to power plant CO2 emission. The percentage of thermal power generation to total power generation (K%) is thus a key parameter for estimating nighttime light by power consumption from thermal power plants. In this study, the DMSP/OLS annual nighttime radiance-calibrated product (RCI) for 2006 and the NSL data corrected by K% were employed to estimate electric power and power plant CO2 emissions. Results indicated that the RCI data can offer more accurate estimates of electric power consumption than can the NSL data. It was also found that NSL and RCI data corrected by K% are good proxies for estimating power plant CO2 emissions.

■

INTRODUCTION Certain human activities have been identified as being significant contributors to recent climate changes, which are often collectively termed global warming.1 Among these changes, the increase in CO2 levels is due in large part to emissions from fossil fuel combustion. According to IPCC 2007,2 CO2 emissions grew by ∼80% from 1970 to 2004, and most CO2 emissions are associated with power generation and transportation. To set CO2 emission targets and to mitigate the effects of greenhouse gas emissions, it is essential to first monitor fossil fuel CO2 emissions. As of this writing, CO2 emissions are obtained mainly from statistical data. Moreover, statistical data are aggregated by administrative region, so it is difficult to study the spatial distribution at resolutions finer than the administrative region. However, satellite remote sensing can be used to estimate the ground surface and atmospheric parameters at the pixel level, albeit with some estimation errors, and is not limited by administrative regions. Thus, estimating CO2 emissions from satellite remote sensing data is an important method. The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) satellite sensor is uniquely suited to monitoring nighttime light distribution from human activities. Many researchers have reported that analysis of DMSP/OLS annual nighttime stable light product (NSL) from human activities is a good candidate for estimating fossil fuel CO2 emissions,3−5 electricity consumption,3,6 population,4,7 and urban expansion,8−10 at the national and regional levels. © 2014 American Chemical Society

However, one limitation of the NSL data is that the high gain setting of 6-bit data quantization with a digital number (DN) range from 0 to 63 and the limited dynamic range of the OLS sensor have saturated DNs in urban centers with strong nighttime light intensity.11 Consequently, pixels of nighttime light images in urban center areas are always valued as 63 even though the actual brightness of nighttime light in those areas should be much larger than 63. Saturation problems in nighttime light data are overcome by the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite12 launched in October 2011.13 However, the annual VIIRS/DNB nighttime light product has not yet been released. The DMSP/OLS nonsaturated annual radiancecalibrated image product (RCI) is available for years 2006− 2010, whereas the NSL data from NOAA/NGDC are available for years 1992−2012. Correction for saturation effects is thus required when using the time-series NSL data, except for RCI data from 2006 to 2010.14 Oda et al.15 redistributed an existing fossil fuel CO2 emissions database using DMSP/OLS RCI for 2006. However, the relationship between RCI intensity and fossil fuel CO2 emission has not been previously investigated. Received: Revised: Accepted: Published: 259

December 23, 2013 April 23, 2014 April 24, 2014 April 24, 2014 dx.doi.org/10.1021/ez500093s | Environ. Sci. Technol. Lett. 2014, 1, 259−265

Environmental Science & Technology Letters

Letter

Table 1. DMSP/OLS Nighttime Light Data and Annual Statistical Data of the Total Electric Power Consumption, Electric Power Consumption by Illumination, Total Electric Power Generation, and Power Plant CO2 Emissions in Electric Power Supply Regions of Japan during 2007 electric power supply region

EPCILLa (×106 kWh)

EPCTOTb (×106 kWh) 137484 63579 32445 29305 150422 297397 88082 29269 84072 7491

EPGTOTc (×106 kWh)

CO2ppd (×106 kg)

Chubu Chugoku Hokkaido Hokuriku Kansai Tokyo Kyusyu Shikoku Tohoku Okinawa electric power supply region

36125 18889 11795 7913 50182 97600 29550 9651 25073 2944 NSL data

RCI data

K%e

165058 64945 95556 24745 43816 11845 40589 16278 166258 32788 354024 117388 127455 21336 61603 9509 129503 30990 8909 3991 K-corrected RCI (RCI × K%) (×106 kg)

Chubu Chugoku Hokkaido Hokuriku Kansai Tokyo Kyusyu Shikoku Tohoku Okinawa

700529 302386 441896 164838 556151 1251093 489916 167226 617772 36034

1573808 568205 1012516 274922 1588042 2989438 941861 298650 1086806 98255

76.05 87.38 66.94 66.86 53.33 73.74 63.08 69.89 70.03 99.48

1196881 496498 677778 183813 846903 2204412 594126 208726 761090 97744

a

Electric power consumption by illumination. bTotal electric power consumption. cTotal electric power generation. dPower plant CO2 emissions. Percentage of thermal power generation to total power generation.

e

settings are used to monitor faint sources of visible and nearinfrared radiance emission from Earth’s surface and moonlit clouds. However, this results in saturated data for many urban areas in the NSL data.17 In RCI data, there are nonsaturated pixels because a fixed gain setting (low, medium, or high) was selected to avoid saturation problems.11 Table 1 shows cumulative DNs of the nighttime light product and annual statistical data of EPCTOT, EPCILL, EPGTOT, CO2pp, and K% for 2007 in 10 electric power supply regions of Japan (Figure 1). The statistical data used in this

Fossil fuel CO2 emissions include emissions from not only power plants but also vehicles and factories. However, nighttime lights in the NSL data are almost all due to illumination in human settlement areas; this illumination uses electric power, which is supplied by power plants.14 Thermal power plants generate electricity and emit CO2 directly by burning fossil fuels. In many Asian countries, thermal power generation accounts for a high percentage of total power generation. Thus, CO2 emissions from power plants (CO2pp) might be more closely related to NSL data than total fossil fuel CO2 emissions (CO2TOT). However, few studies7,15 have investigated the relationship between CO2pp and the NSL data. In this study, the relationship between cumulative DNs of the NSL data and statistical data on electric power consumption by illumination (EPCILL), total electric power consumption (EPCTOT), total electric power generation (EPGTOT), and CO2pp is investigated and used as the basis for proposing a new method of estimating CO2pp. Furthermore, the effects of saturation light correction and the percentage of thermal power generation to total power generation (K%) are investigated to improve the estimation accuracy of CO2pp emissions from nighttime light data.

■

DATA USED DMSP/OLS NSL and RCI nighttime light products for 2006 used in this study are available from the National Oceanic and Atmospheric Administration (NOAA)/National Geographic Data Center (NGDC). The NSL and RCI products with 1 km resolution are produced by all the available cloud-free data for that particular calendar year in the NGDC’s digital archive. The DMSP/OLS was originally designed to detect weak lunar light from nighttime clouds. On cloud-free nights, it is able to monitor lights on Earth’s surface. Low-gain settings are usually used to detect the brightness of urban areas,16 while high-gain

Figure 1. Electric power supply regions in Japan. 260

dx.doi.org/10.1021/ez500093s | Environ. Sci. Technol. Lett. 2014, 1, 259−265

Environmental Science & Technology Letters

Letter

assume a strong linear correlation not only between CDNNSL and statistical values of EPCILL, EPCTOT, and EPGTOT but also between CDNRCI by electric power consumption from thermal power generation (e.g., K-corrected CRI) and CO2pp emissions. To estimate CO2pp from nighttime light data, the relationship between the cumulative DNs of the NSL data (CDNNSL) and statistical values of EPCILL, EPCTOT, EPGTOT, and CO2pp in 10 electric power supply regions in Japan was investigated (Figure 2). DNs of the NSL data of 0.97). The Tokyo and Kansai regions are well above the regression line for the CDNNSL data and EPCILL, EPCTOT, and EPGTOT in panels a, c, and e of Figure 3, probably because of the influence of saturation effects in the NSL data. As shown in Figure 4b, DNs of the NSL data in urban areas, which can be confirmed from land-cover classification data23 (Figure 4c), are nearly saturated. However, there is no saturation area in Figure 4a, which also can be confirmed from Figure 4c. In Figure 4d, it is possible to confirm saturated pixels in the NSL data along the latitude transections. In contrast, there are no saturation pixels in the RCI data along the transections. Cumulative DNs of the NSL data can therefore be underestimated compared to RCI in urban areas with high nighttime light intensity. Indeed, in panels a, c, and e of Figure 3, which use the nonsaturated RCI data, the Tokyo and Kansai regions are close to the regression line (Figure 5a−c). The coefficient of determination increased from 0.84 to 0.91 in the regression analysis among EPCILL, CDNNSL, and CDNRCI. Panels a, c, and e of Figure 3 show that the Hokkaido region is below the regression line even when using the nonsaturated RCI data. This is attributed to overestimation of cumulative DNs in Hokkaido because of light blooming.24 The Chubu and Kansai regions are off the regression line in Figure 3f most likely because its K% is different from those of other regions (Table 1). On the basis of the relationships (R2 > 0.84) seen among the parameters shown in Figure 3, we performed regression analyses between the CDNNSL data and annual total statistical values of CO2pp (Figure 6a). A strong relationship (R2 > 0.83)

■

METHODOLOGY Power plants send generated electricity to human settlement areas, and part of this electricity is used for nighttime lighting in human settlements. The DMSP/OLS satellite sensors can monitor nighttime light at Earth’s surface, and the nighttime stable light in DMSP/OLS NSL data is mostly from human settlements. Because thermal power plants consume fossil fuels and emit CO2 when generating electricity, it should be feasible to estimate CO2pp using the brightness intensity of the NSL data when the following conditions are met. (1) There is a good linear correlation between EPCILL and EPCTOT in each area. In other words, the proportion of consumed power used for illumination (EPCILL%) is almost the same among areas. (2) Thermal power generation accounts for a high percentage of total power generation. Elvidge et al.3 reported strong linear correlations among nighttime stable light, electric power consumption, and CO2 emissions for 200 countries. There are similar linear correlations of electricity consumption for lighting and total electricity consumption at the regional (e.g., state and province) level, but possibly not for CO2 emissions because of regional concentrations of electricity from renewable energy and nuclear power plants, which produce low CO 2 emissions.20,21 However, CO2pp emissions are almost solely emitted from thermal power plants. In this study, we thus 261

dx.doi.org/10.1021/ez500093s | Environ. Sci. Technol. Lett. 2014, 1, 259−265

Environmental Science & Technology Letters

Letter

Figure 3. Relationship among CDNNSL data and statistical values of EPCILL, EPCTOT, EPGTOT, and CO2pp emissions in 10 electric power supply regions of Japan [p values of (a) 1.55 × 10−4, (b) 5.26 × 10−9, (c) 7.16 × 10−4, (d) 1.98 × 10−7, (e) 2.23 × 10−5, and (f) 1.87 × 10−5, all significant at the p < 0.05 level].

regression line as a result of blooming effects, this correction results in a significant improvement in the coefficient of determination (R2) from 0.85 to 0.92 (Figure 6c), and the assumption is demonstrated. From this result, we can also see that K% is an important factor in the accuracy of estimations of CO2pp from NSL and RCI data. A CO2pp estimation method is proposed on the basis of the assumption that both EPCILL% and K% are almost the same among electric power supply regions. However, electricity consumed for industrial uses, heating, cooling, and some commercial purposes could impact the EPCILL% on smaller spatial scales. Thus, differences in EPCILL% within each region could be a source of uncertainty in addition to K% when applying this method to other countries or regions. In such cases, it is better to correct the NSL data by using the formula CDNNSL/EPCILL% instead of the horizontal axis in Figure 6c. Also, accuracy in estimating CO2pp emissions depends on the main types of fossil fuel used in the thermal power plants.

was found between two parameters. The coefficient of determination increased from 0.83 to 0.85 after using the RCI data instead of the NSL data (Figure 6b). However, the Tokyo, Chubu, and Kansai regions are still off the regression line. This is attributed to the K% values in these regions being different from those in other regions. For example, when K% differs in two cities that obtain power from different power plants, the CO2pp per light in the two cities will be different, even if the cumulative DNs of nighttime light in the two cities are the same. Therefore, it is necessary to correct the cumulative DNs of the nighttime light with K% (CDNRCI × K%) and to convert to nighttime light by thermal power generation for more accurate estimation of CO2pp from nighttime light data. To demonstrate the assumption of a linear correlation between nighttime light data and power plant CO2 emissions, we performed regression analysis between K-corrected CDNRCI and statistical values of CO2pp. Although Hokkaido is off the 262

dx.doi.org/10.1021/ez500093s | Environ. Sci. Technol. Lett. 2014, 1, 259−265

Environmental Science & Technology Letters

Letter

Figure 4. Latitudinal transects of the NSL data and nonsaturated RCI data in the Tokyo region for 2006: (a) RCI data, (b) NSL data, (c) land cover classification map, and (d) DNs of the NSL and RCI data along latitudinal transects.

Figure 5. (a−c) Same as panels a, c, and e of Figure 3, respectively, but with RCI data instead of NSL data on the horizontal axis [p values of (a) 1.33 × 10−5, (b) 6.56 × 10−6, and (c) 1.91 × 10−5, all significant at the p < 0.05 level].

263

dx.doi.org/10.1021/ez500093s | Environ. Sci. Technol. Lett. 2014, 1, 259−265

Environmental Science & Technology Letters

Letter

Figure 6. Relationship between CO2pp and CDNNSL for (a) CDNRCI data, (b) cumulative DNs of the K-corrected CDNRCI data, and (c) K-corrected CDNRCI = K% × CDNRCI. K% is the percentage of thermal power generation to total power generation [p values of (a) 2.18 × 10−4, (b) 1.46 × 10−4, and (c) 1.03 × 10−5, all significant at the p < 0.05 level].

in EPCILL% and K% within regions. To address that, it is necessary to correct the nighttime light data, CDNNSL and CDNRCI, by first using EPCILL% and K%. After that, regression analysis between the corrected nighttime CDN data and statistical data about CO2pp emissions can be used. Finally, the regression formula as shown in Figure 6c can be used to estimate CO2pp in regions when the correlation coefficient is sufficient. A method for estimating annual CO2pp emissions was proposed in this study. However, it is preferable to investigate the possibility of estimating the instantaneous value of electric power and power plant CO2 emissions using daily S-NPP/ VIIRS-DNB data and DMSP/OLS nighttime data corrected by the percentage of thermal power generation as a next step. SNPP/VIIRS is a successful satellite sensor for monitoring global Earth environments, during both day and night. The Japan Aerospace Exploration Agency (JAXA) is scheduled to launch the SGLI sensor, the successor to the ADEOS-II/GLI25 satellite sensor, onboard a GCOM-C-series satellite in 2016.26 If it is possible to monitor nighttime light as well as make daytime observations, significant progress can be expected in research on the balance of power generation by fossil fuel consumption and renewable energy such as wind-generated electricity, which has increased in recent years.

Because the CO2 emission factor and percentage of fossil fuel consumption are different with respect to each power plant, CO2pp emissions per thermal power generation are correspondingly different.

■

SUMMARY Regression analysis between the cumulative DNs of DMSP/ OLS nighttime stable light data and statistical data related to electric power was performed with the aim of estimating electric power and power plant CO2 emissions. For overcoming the effects of the saturation issue in stable light data and improving the accuracy of the estimations, nonsaturated radiance-calibrated data and the corrected radiance-calibrated data by percentage of thermal power generation to total power generation (K%) were also employed in the regression analysis instead of stable light data. We conclude that (a) correction of the saturation light effect is essential for estimating electric power generation and consumption and (b) the corrected stable light data and the radiance-calibrated data by K% are good proxies for estimating power plant CO2 emissions. In developed countries, detailed information about individual power plants is often available, and therefore, there is little need for regional estimates of CO2pp emissions in those countries. The method proposed in this study is more useful for estimating the spatial distribution of CO2pp emissions at finer spatial resolutions. The method is also useful for some developing countries that lack information about individual power plants. One caveat for applying this method is that care should be taken to avoid estimation error caused by differences 264

dx.doi.org/10.1021/ez500093s | Environ. Sci. Technol. Lett. 2014, 1, 259−265

Environmental Science & Technology Letters

■

Letter

(16) Elvidge, C. D.; Baugh, K. E.; Kihn, E. A.; Kroehl, H. W.; Davis, E. R.; Davis, C. Relation between satellite observed visible-near infrared emission, population, and energy consumption. International Journal of Remote Sensing 1997, 18, 1373−1379. (17) Elvidge, C. D.; Baugh, K. E.; Dietz, J. B.; Bland, T.; Sutton, P. C.; Kroehl, H. W. Radiance calibration of DMSP-OLS low-light imaging data of human settlements. Remote Sensing of Environment 1999, 68, 77−88. (18) Statistics of Electric Power Industry (SEPI); The Federation of Electric Power Companies of the Japan Electric Association: Tokyo, 2008; Vol. 64−65, p 105 (in Japanese). (19) Global warming promote regional planning guidelines; Global Environment Bureau, Ministry of the Environment: Tokyo, 2003; p 164 (in Japanese). (20) Rayner, P. J.; Raupach, M. R.; Paget, M.; Peylin, P.; Koffi, E. A new global gridded data set of CO2 emissions from fossil fuel combustion: Methodology and evaluation. J. Geophys. Res. 2010, 115, D19306. (21) Kiran Chand, T. R.; Badarinath, K. V. S.; Elvidge, C. D.; Tuttle, B. T. Spatial characterization of electrical power consumption patterns over India using temporal DMSP/OLS night time satellite data. International Journal of Remote Sensing 2009, 30 (3), 647−661. (22) Zhao, N.; Currit, N.; Samson, E. Net primary production and gross domestic product in China derived from satellite imagery. Ecol. Econ. 2011, 70, 921−928. (23) Tateishi, R.; Uriyangqai, B.; Bilbisi, H. A.; Ghar, M. A.; Ayush, J. T.; Kobayashi, T.; Kasimu, A.; Hoan, N. T.; Shalaby, A.; Alsaaideh, B.; Enkhzaya, T.; Gegentana; Sato, H. P. Production of global land cover data-GLCNMO. International Journal of Digital Earth 2011, 4, 22−49. (24) Small, C.; Pozzi, F.; Elvidge, C. D. Spatial analysis of global urban extent from DMSP-OLS night lights. Remote Sensing of Environment 2005, 96, 277−291. (25) Nakajima, T. Y.; Nakajima, T.; Nakajima, M.; Fukushima, H.; Kuji, M.; Uchiyama, A.; Kishino, M. Optimization of the Advanced Earth Observing Satellite II Global Imager channels by use of radiative transfer calculations. Appl. Opt. 1998, 37, 3149−3163. (26) Letu, H.; Nakajima, T. Y.; Matsui, T. N. Development of an ice crystal scattering database for the global change observation mission/ second generation global imager satellite mission: Investigating the refractive index grid system and potential retrieval error. Appl. Opt. 2012, 51, 6172−6178.

AUTHOR INFORMATION

Corresponding Author

*Research and Information Center, Tokai University, 4-1-1 Kitakaname Hiratsuka, Kanagawa 259-1292, Japan. Telephone: +81-463-58-1211, ext. 4913. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We thank the NOAA/NGDC for providing DMSP/OLS nighttime stable light product and annual radiance calibration data for 2006. This work was supported by the GCOM-C/ SGLI project of the Japan Aerospace Exploration Agency (JAXA), and the Japan Science and Technology Agency (JST), CREST/EMS/TEEDDA.

■

REFERENCES

(1) Advancing the Science of Climate Change (ASCC). America’s Climate Choices: Panel on Advancing the Science of Climate Change; The National Academies Press: Washington, DC, 2010; pp 21−22. (2) Metz, B.; Davidson, O. R.; Bosch, P. R.; Dave, R.; Meyer, L. A. Intergovernmental Panel on Climate Change (IPCC). Climate Change Working Group III: Mitigation of Climate Change; Cambridge University Press: Cambridge, U.K., 2007; pp 97−105. (3) Elvidge, C. D.; Imhoff, M. L.; Baugh, K. E.; Hobson, V. R.; Nelson, I.; Dietz, J. B. Nighttime light of the world: 1994−1995. ISPRS Journal of Photogrammetry and Remote Sensing 2001, 56, 81−99. (4) Doll, C. N. H.; Muller, J. P.; Elvidge, C. D. Nighttime imagery as a tool for globe mapping of socioeconomic parameters and greenhouse gas emissions. Ambio 2000, 29, 157−162. (5) Letu, H.; Hara, M.; Yagi, H.; Naoki, K.; Tana, G.; Nishio, F.; Okada, S. Estimating energy consumption from nighttime DMSP/ OLS imagery after correcting for saturation effects. International Journal of Remote Sensing 2010, 31, 4443−4458. (6) Ayanu, Y. Z.; Conrad, C.; Nauss, T.; Wegmann, M.; Koellner, T. Quantifying and mapping ecosystem services supplies and demands: A review of remote sensing applications. Environ. Sci. Technol. 2012, 46, 8529−8541. (7) Raupach, M. R.; Rayner, P. J.; Paget, M. Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions. Energy Policy 2010, 38, 4756−4764. (8) Small, C.; Pozzi, F.; Elvidge, C. D. Spatial analysis of global urban extent from DMSP-OLS night lights. Remote Sensing of Environment 2005, 96, 277−291. (9) Small, C.; Elvidge, C. D.; Balk, D.; Montgomery, M. Spatial scaling of stable night lights. Remote Sensing of Environment 2011, 115, 269−280. (10) Zhang, Q.; Seto, K. C. Mapping urbanization dynamics at regional and global scales using multi-temporal dmsp/ols nighttime light data. Remote Sensing of Environment 2011, 115, 2320−2329. (11) Ziskin, D.; Baugh, K.; Feng, C. H.; Ghosh, F.; Elvidge, C. D. Methods Used For the 2006 Radiance Lights. Proceedings of the 30th Asia-Pacific Advanced Network Meeting 2010, 131−142. (12) Miller, S. D.; Mills, S. P.; Elvidge, C. D.; Lindsey, D. T.; Lee, T. F.; Hawkins, J. D. Suomi satellite brings to light a unique frontier of nighttime environmental sensing capabilities. Proc. Natl. Acad. Sci. U.S.A. 2012, 109 (39), 15706−15711. (13) Cao, C.; Shao, X.; Uprety, S. Detecting Light Outages After Severe Storms Using the S-NPP/VIIRS Day/Night Band Radiances. IEEE Geoscience and Remote Sensing Letters 2013, 10, 1582−1586. (14) Letu, H.; Hara, M.; Tana, G.; Nishio, F. A saturated light correction method for DMSP/OLS nighttime satellite imagery. IEEE Transactions on Geoscience and Remote Sensing 2012, 50, 389−396. (15) Oda, T.; Maksyutov, S. A very high-resolution (1km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos. Chem. Phys. 2011, 11, 543−556. 265

dx.doi.org/10.1021/ez500093s | Environ. Sci. Technol. Lett. 2014, 1, 259−265