Impact of the Green Light Program on haze pollution in 1 the North China Plain , China 2

Abstract. As the world's largest developing country, China undergoes the ever-increasing demand for electricity during the past few decades. In 1996, China launched the Green Lights Program (GLP), which becomes a national energy conservation activity for saving lighting electricity, as well as an effective reduction of the coal consumption for power generation. Despite of the great success of the GLP, its effects on haze pollution have not been investigated and well understood. This study focused to assess the potential coal-saving induced by the GLP and to estimate the consequent improvements of the haze pollutions in the North China Plain (NCP), because severe haze pollutions often occur in the NCP and a large amount of power plants locate in this region. The estimated potential coal-saving induced by the GLP can reach a massive value of 120–323 million tons, accounting for 6.7–18.0 % of the total coal consumption for thermal power generation in China. In December 2015, there was a massive potential emission reduction of air pollutants from thermal power generation in the NCP, which was estimated to be 20.0–53.8 Gg for NOx and 6.9–18.7 Gg for SO2. The potential emission reductions induced by the GLP played important roles in the haze formation, because the NOx and SO2 are important precursors for the formation of particles. To assess the impact of the GLP on haze pollution, sensitive studies were conducted by applying a regional chemical/dynamical model (WRF-CHEM). The model results suggest that in the lower limit case of emission reduction, the PM2.5 concentration decreases by 2–5 µg m−3 in large areas of the NCP. In the upper limit case of emission reduction, there was much more remarkable decrease in PM2.5 concentration (4–10 µg m−3). This study is a good example to illustrate that scientific innovation can induce important benefits on environment issues, such as haze pollution.



Introduction
As the world's largest developing country, China undergoes the ever-increasing demand for electricity during the past few decades.Artificial lighting is an important part of China's energy consumption, accounting for a quite stable share of about 10-14% of the total electricity consumption (Lv and Lv, 2012;Zheng et al., 2016).Also, the lighting demand in China is predicted to increase continuously, with a projected average annual growth rate of 4.3% from 2002 to 2020 (Liu, 2009).With principal objective of alleviating shortage of electricity, China has launched the Green Lights Program (GLP) in 1996, with the core of aiming at replacing low-efficiency lighting lamps by high-efficiency ones.Since then, the GLP has become a national energy conservation activity for saving lighting electricity, and has been highlighted continuously in the nation's 9 th -12 th Five-Year Plan (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) (Lin, 1999).
With the object of providing high-quality efficient lighting products, the GLP is undoubtedly a useful electricity-saving measurement.Nonetheless, driven by the accelerated economic increase, the thermal power electricity has experienced an ever-increasing trend in the past decades, as well as the associated coal consumption for thermal power generation.Thermal power generation is the primary electricity source in China, contributing to about 72-78% out of the total electricity (NBS, 2000(NBS, -2016)).In 2015, the coal consumption for thermal power generation in China raise to a very massive value of about 1.8 billion tons, which is 3.2 times of that in 2000.Simultaneously, the coal consumption for thermal power generation is 2.7 times of that in the USA in 2015, which is reported to be 670 million tons (https://www.eia.gov/totalenergy/data/browser/, last accessed on 20 December, 2018).
Due to the significant use of coal, thermal power generation is one of the dominant emission contributors to anthropogenic air pollutants in China (Tie and Cao, 2010;Wang and Hao, 2012; Atmos.Chem.Phys.Discuss., https://doi.org/10.5194/acp-2018-1319Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 14 March 2019 c Author(s) 2019.CC BY 4.0 License.Wang et al., 2015b).The power sector contributes significantly to air pollutants of the nitrogen oxides (NOx), the sulfur dioxide (SO 2 ), and the particulate mater (PM) (Zhao et al., 2013;Huang et al., 2016).The pollutants of SO 2 and NOx are the precursors of secondary pollutant of ozone (O 3 ), and secondary aerosols (Seinfeld et al., 1998;Laurent et al., 2014).It is also reported that emission from power sector is a major contributor to particulate sulfate, and nitrate (Zhang et al., 2012).The emissions from thermal power generation in China can also transport to a long distance, causing regional/global air pollutions (Tie et al., 2001;Huang et al., 2016).Considering the important contributions to air pollutants, controlling emissions from thermal power generation is a vital strategy for the improvement of air quality in China.
Distinguished from the ever-increasing trend of thermal power electricity and associated coal consumption, the increase trends of SO 2 and NOx emissions from thermal power generation are curbed and even change to decrease (Liu et al., 2015).This is caused by the famous nation-wide project of utilizing emission control facilities during 2005 to 2015, such as installing flue-gas desulfurization/denitrification systems and optimizing the generation fleet mix (Liu et al., 2015;Huang et al., 2016).Given the technological changes that have occurred in the power sector, the air pollutant emissions from power generation have been significantly reduced.However, the thermal power generation is still identified to be with massive air pollutant emissions, involving 5.1 million tons of NOx, 4.0 million tons for SO 2 , and 0.8 millions tons of PM in 2015.Under high standards of ultra-low emission power units, the staggering total amount of coal consumption becomes a vital challenge for emission control from thermal power generation.
With ambitious and comprehensive efforts, the success of the GLP resulted in about 59 billion kWh of accumulated electricity savings from 1996 to2005 (SCIO, 2006), and about 14.4 Coordinate with the effectiveness of the GLP on energy saving, the effects of power generation or coal-saving on air quality are elaborated in previous studies (Liu et al., 2015;Huang et al., 2016;Hu et al., 2016).
However, few studies have been so far dedicated to estimate the effectiveness of the GLP in controlling air pollution on a regional scale, especially in North China Plain (NCP).In the NCP, the thermal power plants are very densely distributed, resulting in massive emissions of air pollutants (Liu et al., 2015).As a result, the GLP could produces significant energy-saving and reduces the associated air pollutant emissions from thermal power generation.Although the GLP is under the strong and sustained government commitment, however, there is no built-in mechanism for monitoring the GLP and without regularly issued official program assessment reports (Guo and Pachauri, 2017).During the past decades, the Chinese government has published only one report regarding the performance of the GLP (NDRC, 2005).There are several articles and books for summarizing the GLP from time to time by the Energy Research Institute under Chinas' NDRC, providing additional information for assessments (Yu and Zhou, 2001;Liu, 2006;Liu and Zhao, 2011;Liu, 2012;Lv and Lv, 2012;Gao and Zheng, 2016).
Previous studies do not well investigate the effects of the GLP on air pollution, such as the resultant of emission reductions of air pollutants, or the consequent effects on haze pollution.
In the present study, we quantified the effect of the GLP on the haze pollution in the NCP, a severe air polluted region in China.The study included satellite measurements and numerical model studies (WRF-CHEM).We first investigated the lighting coal consumption and resultant coal-saving induced by the GLP utilizing the satellite nighttime lights (NTL) data (Elvidge et al., 2009), which has been widely used to estimate the consumption of energy and electricity (He et al., 2013;Huang et al., 2014).Then we evaluated the potential emission reductions and resultant effects on air pollution in the NCP using the WRF-CHEM model.This study provided an overall perspective on gaps of the unevaluated potential benefits to haze pollution induced by the GLP, which can inspire more macroscopic and interdisciplinary analysis in long-term national activities based on NTL datasets.We summarized the data, the methodology, and the WRF-CHEM model description in Section 2. Results and discussions were presented in Section 3, followed by the summaries and conclusions in Section 4.

The long-term NTL data and coal consumption
In order to understand the spatial distributions of lighting before and after the GLP in China, we investigated the version 4 of the Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) NTL time series data from 1992 to 2013 (Elvidge et al., 2014).
The dataset available at: https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html.We selected the stable light datasets, which are the cloud-free composites using all the archived DMSP/OLS smooth resolution data for calendar years.The images represent the average intensity of NTL with DN values ranging from 0 to 63 in 30 arc-second grids-cells (about 1 km spatial resolution).The 1992 and the 2013 datasets were used to investigate the different overview status of NTL before and after the GLP for long years.Considering the differences between the sensors, differences in the crossing times of the satellites, and degradation of the sensors (Elvidge et al., 2009;Elvidge et al., 2014), we inter-calibrated the NTL datasets Atmos.Chem.Phys.Discuss., https://doi.org/10.5194/acp-2018-1319Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 14 March 2019 c Author(s) 2019.CC BY 4.0 License.followed a second order regression model (Elvidge et al., 2014).
Figure 1 shows the spatial distributions of the DMSP/OLS NTL data.We found that the lighting usages were significantly increased from 1992 to 2013, both in lighting intensity and spatial coverage, especially in the regions of eastern China, including the NCP, the Pearl River Delta, and the Yangtze River Delta.The rapid increase in the usage of lighting suggested that the generations of electricity were greatly enhanced.
Figure 2 shows a long-term evolution of thermal power electricity and coal consumption for power generation.It shows that the thermal power electricity increased from 2000 (about 10 12 kW h) to 2015 (about 4×10 12 kW h), indicating that due to the rapid increase in the economics, the demand of electricity was largely enhanced in China.The emission of SO 2 increased before 2006, due to the increase of coal consumption.While after 2006, although the coal consumption still increased, the emission of SO 2 started to decrease, suggesting that the desulfurization played important roles in the emission reductions from thermal power generation (Liu et al., 2016).The decrease of NOx emission started to decrease in 2012, which was 6 year later than the decrease in SO 2 emissions, suggesting that the denitrification played important roles in the emission reduction from thermal power generation after 2012 (Hu et al., 2016).Compared to the gas-phase emissions of SO 2 and NOx, the direct emission of particles (PM 2.5 ) was relatively small (Liu et al., 2015).The large portion of gas-phase emissions from thermal power generation indicated that the most PM 2.5 emitted from the power plants might be in the phase of secondary particles.
The above long-term variability of thermal power electricity and associated coal consumption for power generation was based on the situation that the GLP was conducted in China, which could produce a strong reduction for the coal burning emissions from thermal power generation, such as air pollutants of SO 2 and NOx.These gases might have important effects on the PM 2.5 pollution in China, because they are important precursors for the production of particle matter (Seinfeld et al., 1998;Laurent et al., 2014).However, as the business as usual condition (i.e., without the GLP), the increased lighting demand could cause significant increase in thermal power electricity, and the associated growth of coal consumption for power generation during the past decades.This study was to assess the potential effects induced by the GLP on the severe haze pollution in the NCP (Tie et al., 2017;Long et al., 2018), and also displayed a good example to illustrate that scientific innovation can induce important benefits on environment issues.To assess the impacts of the GLP on the severe air polluted region in China, such as in the NCP, several important tools and data were used in this study, including a regional chemical/dynamical model (WRF-CHEM), satellite data (DMSP/OLS and S-NPP), and surface measurements of air pollutants.

Description of the WRF-CHEM model
We used a specific version of the WRF-CHEM model (Grell et al., 2005).The model included a new flexible gas-phase chemical module and the Models3 community multi-scale air quality (CMAQ) aerosol module developed by the US EPA (Binkowski and Roselle, 2003).The model included the dry deposition (Wesely 1989) and wet deposition followed the CMAQ method.The impacts of aerosols and clouds on the photochemistry (Li et al., 2011b) were considered by the photolysis rates calculation in the fast radiation transfer model (Tie et al., 2003;Li et al., 2005).The inorganic aerosols (Nenes et al., 1998) were predicted using the ISORROPIA Version 1.7.We also used a non-traditional secondary organic aerosol (SOA) model, including the volatility basis-set modeling approach and SOA contributions from glyoxal and methylglyoxal.Detailed information about the WRF-CHEM model can be found in previous studies (Li et al., 2010;Li et al., 2011a;Li et al., 2011b;Li et al., 2012).
In the present study, we simulated severe haze pollution from 1 to 31 December 2015 in the NCP.The domain, centered at the point of (116° E, 38° N), was composed horizontally of 300 by 300 grid points spaced with a resolution of 6 km (Fig. 3) and vertically with 35 sigma levels.
The physical parameterizations included the microphysics scheme (Hong and Lim 2006), the Mellor-Yamada-Janjic turbulent kinetic energy planetary boundary layer scheme (Janjić, 2002), the unified Noah land-surface model (Chen and Dudhia, 2001), the Goddard long wave radiation parameterization (Chou and Suarez, 1999), and the shortwave radiation parameterization (Chou et al., 2001).Meteorological initial and boundary conditions were obtained from the 1° by 1° reanalysis data of National Centers for Environmental Prediction (Kalnay et al., 1996).The spin-up time of WRF-CHEM model is 3 days.The chemical initial and boundary conditions were constrained from the 6 h output of Model of Ozone and Related chemical Tracers, Version 4 (Horowitz et al., 2003).
We utilized the anthropogenic emission inventory developed by Tsinghua University (Zhang et al., 2009), including anthropogenic emission sources from transportation, agriculture, industry and power generation and residential.The dataset can be accessible from the website of MEIC (http://www.meicmodel.org),providing for the community a publically accessible emission dataset over China with regular updates.The emission inventory used in the present study is updated and improved for the year 2015.In addition, the emissions of SO 2 , NOx, and CO have been adjusted according to the observations during the period.Emissions from biogenic sources were calculated online using the Model of Emissions of Gases and Aerosol from Nature model (MEGAN) (Guenther et al. 2006).widely used in recent studies, which confirmed to establish empirical relationships with energy use (Román and Stokes, 2015;Coscieme et al., 2014).To some extent, the VIIRS NTL dataset (in 15 arc-second grids-cells, about 500 m) are superior to the DMSP/OLS NTL dataset (Elvidge et al., 2013).In the present study, we used the version 1 of VIIRS NTL dataset to investigate the consumption of lighting electricity in each province, defined as provincial dynamics as follow.
where i denotes the provincial domain, and w is the nationwide domain.j is the pixel of VIIRS NTL dataset.S is the area of pixel j.L is the NTL radiance.The annual VIIRS NTL dataset contains cloud-free average of NTL radiance by excluding any data impacted by stray light, and further screening out the fires and other ephemeral lights and background (non-lights).The dataset is available at: https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html.
The distribution of VIIRS NTL radiance in 2015 (Fig. S1) was similar as the DMSP/OLS DN values (Fig. 1).The high values of annual NTL radiance were concentrated in the densely populated and industrial developed areas of China (Fig. S1a), such as the NCP, the Yangtze River Delta, and the Pearl River Delta.There were "hot spot" located in some megacities, such as the Beijing, Tianjin, Shanghai, Guangzhou, where the NTL radiance can reach as high as 20 mW/m 2 /sr.Statistically, 12.8% of these China's land areas consumes 58.3% of lighting electricity consumption.The high values of provincial dynamics also concentrated on these regions, and all the provincial dynamics exceeding 5% were coastal cities (Fig. S1b).In the NCP, in addition to the high usage of lighting, there is a large amount of power plants (Liu et al., 2015).We selected the NCP (Fig. 3) as the region of interest.In addition, there are extensive measurement sites of pollutants in the domain (the green crosses in Fig. 3).

Estimation of coal-saving induced by the GLP
According to the analysis for the Chinese GLP program (Guo and Pachauri, 2017), the lighting activities can be defined as three clusters according to their usages: (C 1 ) For outdoor lighting, such as road lights; (C 2 ) household usage, mainly for residential applications; (C 3 ) commercial and industrial buildings.In practice, the core of the GLP is to improve luminous efficiency, replacing low-efficiency lighting lamps by high-efficiency ones.The details of the GLP program were as follows.For C 1 , the High Pressure Sodium lamps (HPS) and Metal Halide (MH) lamps are primarily used to replace High Pressure Mercury-vapor lamps (HPM).For C 2 , the Compact Fluorescent Lamps (CFLs) are used to replace incandescent lamps (ILs).For C 3 , the T8/T5 fluorescent tubes are used to replace T12/T10 fluorescent tubes.The emerging LED lamps were not covered, however, it promotes to each of the above cluster (Pan, 2018;Wang, 2017;Asolkar and Dr., 2017;Xie et al., 2016;Ge et al., 2016;Edirisinghe et al., 2016).Here the LED lamps were allocated proportionally based on the proportions of the lighting electricity consumption of C 1 , C 2 , and C 3 .
According to the classification above, we estimated the current equivalent luminous efficacy (!"! !"# ) weighted by the proportion of their lighting electricity consumption.To investigate the potential effectiveness of the GLP, we also calculated the equivalent luminous efficacy without the implementation of the GLP (!"! !"!!"# ).where k denotes the specified cluster of lighting lamps.!! is the proportion of lighting electricity consumed by the k th cluster lamps; !" !,!"# and !" !,!"!!"# denote the equivalent luminous efficacy of the k th cluster lamps with and without the improvement of lighting efficacy induced by the GLP.The ELE is a comprehensive parameter to reflect the lighting efficacy.In terms of current consumption levels of lighting electricity, the lighting coal consumption for power generation is proportional to ELE.As a result, the potential coal-saving induced by the GLP (!") can be estimated by: where !!denotes the current coal consumption for thermal power generation.To get the spatial distribution of potential provincial coal-savings (!" ! ), we spatially scaled the total potential coal-saving (!" ) according to the provincial dynamics factor (PD i ), which is calculated based on the spatiotemporal dynamic of electric power consumption in each province (Elvidge et al., 1997;Chen and Nordhaus, 2011;He et al., 2013).
where i denotes the province; !" !reflects provincial dynamics of lighting coal consumption, which was explained in Eq. 1.
To estimate the emission reduction induced by the GLP, we assumed that the potential emission reduction was mainly due to the emissions from the thermal power plants.Based on the current anthropogenic emission inventory from MEIC (Multi-resolution emission inventory for China) (Liu et al., 2015;Zhang et al., 2009), the potential emission reduction (!" !"!"#,!"#$ ) induced by the GLP was proportional to the associated potential coal-saving for the thermal power generation.where !!"#$%,!"#$ denotes the emission inventory from the thermal power sector; spec is the specify air pollutant of WRF-CHEM species.!" and !! are the same as that in Eq. 4.

WRF-CHEM sensitive studies
Based Zheng, 2016).Here we treated the marketing share of LED lamps as the proportion of its lighting electricity consumption.Then it was allocated proportionally to the clusters according to the research of Zheng et al., (2016), which reported the proportion of its lighting electricity consumption with C 1 : C 2 : C 3 being 31.6%:19.7%: 48.7%.More detailed information can be founded in Table 1.
The estimated ELE values have uncertainties for both low and high efficient lamps, ranging from 52.8 to 57.7 lm/W and from 96.2 to 120.9 lm/W for the ELE with or without the GLP, respectively (see Table 1).In addition, the estimate of lighting electricity accounts for 10-14% of the total electricity (Zheng et al., 2016;Lv and Lv, 2012).As a result, the model sensitive studies included low-limit and high-limit of electricity power savings.To account for all of the uncertain ranges, in the lower limit model simulation, the thermal power was estimated to increase 6.7%, without the GLP.In the higher limit model simulation, the thermal power was estimated to increase 18.7%, without the GLP. Figure 4 shows that under lower and higher limit assumptions, the potential coal-savings induced by the GLP were 120-323 million tons, respectively.According to these estimates into the reference emission inventory (! !,!"#$ ), the emission of pollutants, with the 3 cases (reference, low-limit, and high-limit) were estimated and shown in Table 2.The reference emission inventory is developed by Tsinghua University (Zhang et al., 2009), including current emission levels of thermal power plants (with considering GLP).
Table 2 also shows that the direct emission of PM 2.5 was much smaller than the direct emission of SO 2 and NOx in gas-phase.The PM 2.5 concentrations included two different parts from thermal power plants.One was from the direct emission of PM 2.5 in particle phase, and the other was the secondary particle (PM 2.5 ), which was formed from the chemical transformation from SO 2 and NOx.As a result, the large effect of the GLP on haze pollutions was due to the changes in the emissions of SO 2 and NOx from the thermal power plants.

Model evaluation
To better understand the effect of the GLP on the haze pollution in the NCP, we first conducted an evaluation of the WRF-CHEM model performance.The modeled results were compared to the hourly near-surface concentrations of CO, SO 2 , NO 2 , and PM 2.5 .The data was measured by the China's Ministry of Environmental Protection (MEP), and are accessible from the website http://www.aqistudy.cn/.The locations of the measurement sites show in Fig. 3.
The model results were evaluated by calculating the following statistical parameters, including normalized mean bias (NMB), the index of agreement (IOA), and the correlation coefficient (r).
These parameters were used to assess the performance of REF case in simulations against measurements.
where !! and !! are the calculated and observed air pollutant concentrations respectively.N is the total number of the predictions used for comparisons.! and !represent the average predictions and observations, respectively.The IOA ranges from 0 to 1, with 1 showing perfect agreement of the prediction with the observation.The r ranges from -1 to 1, with 1 implicating perfect spatial consistency of observation and prediction.Although there was a similarity of the temporal variability between PM 2.5 and CO, the magnitude of the variability of CO was smaller than variability of PM 2.5 , suggesting that in addition to the meteorological conditions, the chemical formation also played important roles for producing the high peaks of PM 2.5 concentrations.It is important to simulate the measured temporal variations of SO 2 and NOx, because they are important chemical precursors (Seinfeld and Pandis, 1998;Laurent. et al., 2014), and are the major pollutants emitted from the thermal power plants (Table 2).As shown in Fig. 5, both the measured and modeled SO 2 and NOx had several episodes, which were corresponding to the episodes of the PM 2.5 .The parameters between the measured and modeled results were acceptable, with the IOA of 0.83 and the NMB of 1.3% for SO 2 , and IOA of 0.93 and the NMB of 6.1% for NOx.It is interesting to note that the occurrences of the peak of SO 2 and NOx are about 1-2 days ahead of the peak of PM 2.5 .
One of the explanations was that there was chemical conversion from gas-phase of SO 2 and NOx to particle phase of PM 2.5 , resulting in the time lag between the peaks of SO 2 -NOx and PM 2.5 , because SO 2 and NOx were the precursors of PM 2.5 (Seinfeld and Pandis, 1998;Laurent. et al., 2014).As we state in the previous sections, the large effect of the GLP on haze pollutions was due to the changes in the emissions of SO 2 and NOx from the thermal power plants.The good statistical performance of the modeled SO 2 and NOx provided confident to use the model to study the GLP effects on haze in the NCP region.
In order to do more thoughtful validation of the model performance, Figure 6 shows the measured and modeled spatial distributions of PM 2.5 , SO 2 , and NOx in the NCP.The model generally reproduced the spatial variations of PM 2.5 , NO 2 , and SO 2 , capturing the spatial characters.For example, the SO 2 were largely emitted from thermal power plants and steel industrials, which were large point sources.As a result, both the modeled and measured SO 2 appeared as scattered distributions (see Fig. 6d).The correlation coefficients (r) between the measured and modeled results were 0.86, 0.68, and 0.70 for PM 2.5 , NO 2 , and SO 2 , respectively.

Potential benefit of the GLP to air pollution in the NCP
There are massive emissions of NOx and SO 2 from thermal power plants in the research domain, producing 299.1 Gg and 103.7 Gg (Tab. 1) during the December 2015, for NOx and SO 2 , respectively.There is more emission amount of NOx than SO 2 , because the SO 2 emissions from power had been significantly declined since 2005, whereas the NOx emissions were slightly declined (see Fig. 2) due to lower effective NOx emission control facilities (Liu et al., 2015;Huang et al., 2016).
According to the estimate of 6.7-18.0% of potential coal-saving induced by the GLP (Sect. 2.5), the potential emission reductions from power generation were calculated base on Eq. 6, and the emission reductions of NOx and SO 2 induced by the GLP were estimated for the WRF-CHEM model sensitive studies.Figure 7 shows the spatial distributions of changes in NOx and SO 2 emissions in the research domain, especially the provinces of Hebei, Henan, and Shandong within the NCP, where concentrated most of the power plants (Liu et al., 2015).The results show that under low limit estimate, without the GLP, the NOx and SO 2 emissions would be increased by 20.0 Gg and 6.9 Gg, respectively, in December 2015.Under high limit estimate, without the GLP, the NOx and SO 2 emissions would be increased by 53.8 Gg and 18.7 Gg in the NCP.These large emission changes without the GLP could cause important effects on the air pollution.In the following sections, the GLP effect on the reduction of air pollution was investigated by using the WRF-CHEM model.According to the lower and upper limits of emission reductions induced by the GLP, we evaluated their resultant effects on air pollutants (PM 2.5 , NO 2 , and SO 2 ), which are estimated by the difference of the SEN-GLP cases and the REF case (Fig. 8).The result shows that the GLP has important effects on PM 2.5 concentrations (see Figs 8a and 8b), implicating the remarkable benefit to haze pollution in the NCP.In the lower limit case, the PM 2.5 concentrations could be decreased by 2-5 µg m -3 in large areas within the NCP, such as the southeastern Hebei, northeastern Henan, and western Shandong (Fig. 8a).In the upper limit case, there is much more remarkable decrease in PM 2.5 concentrations (4-10 µg m -3 ) in wider areas within the NCP (Fig. 8b).We can also find large-scale reductions of NO 2 and SO 2 in the NCP (Fig. 8c-f).For example, in high limit case, the reduction of NO 2 ranges from 1-8 µg m -3 , and the reduction of SO 2 ranges from 1-4 µg m -3 .We also display the species variations (PM 2.5 , NO 2 , and SO 2 ) in Fig. S2 within the areas with high PM 2.5 changes induced by the GLP (see red-square in Fig. 8).
Although the influence of the GLP is to decrease PM 2.5 concentrations, there were some slight increase in PM 2.5 concentrations in north of NCP.As indicated in Table 2, the directly emission of PM 2.5 was less than the gas-phase emissions of NOx and SO 2 , which suggested that the decrease of PM 2.5 by applying the GLP was mainly due to the chemical conversions from gas-phase NOx and SO 2 to nitrate and sulfate particles (Seinfeld et al., 1998;Laurent et al., 2014).The slight increase of the PM 2.5 concentrations may be induced by the changes in O 3 concentrations, because the chemical conversion from NOx and SO 2 to nitrate and sulfate requires the atmospheric oxidants like O 3 .As shown in Fig. S3, there is slight increase of O 3 (1-2 µg m -3 ) due to the GLP, and the slightly increase the oxidation of SO 2 , which may cause some enhancement of sulfate concentrations (Wang et al., 2015a;Xue et al., 2016).Apparently,

Summary
For replacing low-efficiency lighting lamps by high-efficiency ones, the Green Lights Program (GLP) is a national energy conservation activity for saving lighting electricity consumption in China, resulting in an effective reduction of coal consumption for power generation.However, despite of the great success of the GLP in lighting electricity, the effects of the GLP on haze pollution are not investigated and well understood.In the present study, we try to assess the potential coal-saving induced by the GLP, and to estimate its resultant benefit to the haze pollutions in the NCP, China, where often suffer from severe haze pollutions.First, we used the satellite dataset of nighttime lights to evaluate the associated saving of lighting electricity consumption and its resultant coal-saving in the NCP.Second, we estimated the emission reductions from thermal power generation induced by the GLP, based on the emission inventory developed by Tsinghua University (Zhang et al., 2009).Finally, we applied the Tab.1        The total emission reductions are also shown in the rectangle.
on previous studies (Guo and Pachauri, 2017), the effective luminous efficacy (ELE) increased from 50 lm/W to 70-140 lm/W for C 1 , from 15 lm/W to 50-60 lm/W for C 2 , and from 70-80 lm/W to 80-105 lm/W for C 3 .Simultaneously, the LED has experienced a fast growth since 2011, with the marketing share of LED lamps reached 32% in 2015, and the high efficacy LED lamps with 150 lm/W had been industrialized production in China (Gao and Atmos.Chem.Phys.Discuss., https://doi.org/10.5194/acp-2018-1319Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 14 March 2019 c Author(s) 2019.CC BY 4.0 License.
Atmos.Chem.Phys.Discuss., https://doi.org/10.5194/acp-2018-1319Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 14 March 2019 c Author(s) 2019.CC BY 4.0 License.In general, the NCP encountered severe haze pollution events during the December 2015.The statistical analysis showed that the WRF-CHEM model reasonably captured the spatial and temporal variations of haze pollution in the NCP, although some model biases existed.The model validation provided a confident to the further model studies.
Atmos.Chem.Phys.Discuss., https://doi.org/10.5194/acp-2018-1319Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 14 March 2019 c Author(s) 2019.CC BY 4.0 License. the NO 2 reductions are more remarkable because of the more noteworthy NOx emission reductions induced by the GLP.The GLP resulted in significant reduction of potential pollutant emissions from the thermal power generation, corresponding to potential benefit in alleviating haze pollution in the NCP, although with few fluctuated deteriorations.It also benefits the pollution of NOx and SO 2 in the NCP.
WRF-CHEM model to evaluate the potential effects of the GLP on the haze pollutions in the NCP.The model results had been evaluated by a comparison with surface measurements.And two sensitivity experiments were conducted to explore the role of the GLP in benefiting the haze pollution.Some important results are summarized as follows.1. Due to the rapid increase in the economics, the demand of electricity is largely enhanced in Atmos.Chem.Phys.Discuss., https://doi.org/10.5194/acp-2018-1319Manuscript under review for journal Atmos.Chem.Phys.Discussion started: 14 March 2019 c Author(s) 2019.CC BY 4.0 License.

Figure 1 .
Figure 1.The spatial distributions of the Nighttime-light data (NLT) from DMSP/OLS DN values in (a) 1992 and in (b) 2013.

Figure 2 .
Figure 2. Coal-fired power electricity and associated coal consumption for power generation, and the emissions of NOx, SO 2 , and PM 2.5 from thermal power plants from 2000 to 2015 in China.

Figure 3 .
Figure 3.The horizontal domain of the model (WRF-CHEM), with the location of sampling sites (shown by the green crosses), and topographical conditions of the NCP, which are surrounded by the Mountains of Yan and Tai in the north and west, respectively.

Figure 4 .
Figure 4.The (a) lower and (b) upper limits of potential coal-savings induced by the GLP.

Figure 5 .
Figure 5.The temporal variations of predicted (red lines) and observed (black dots) profiles of near-surface mass concentrations of PM 2.5 , NO 2 , SO 2 , and CO averaged over all ambient monitoring sites in the NCP during December 2015.

Figure 6 .
Figure 6.The spatial comparisons of predicted and observed episode-average mass concentrations of PM 2.5 , NO 2 , and SO 2 .(a) Statistical comparison of predicted and observed mass concentrations, with the correlation coefficient (r).Horizontal distributions of predictions (color contour) and observations (colored circles) of (b) PM 2.5 , (c) NO 2 , and (d) SO 2 , along with the simulated wind fields (black arrows).

Figure 7 .
Figure 7.The potential emission reductions for low (left panels) and high (right panels) limit cases induced by the GLP, including the mass rates change of (a) NO x , and (b) SO 2 .

Figure 8 .
Figure 8.The lower (left panels) and upper (right panels) episode-averaged variations induced by GLP, including the mass concentrations (µg m -3 ) of (a) PM 2.5 , (b) NO 2 , and (c) SO 2 .The results refer to the spatial variations between the REF case and the SEN-GLPs case (REF − SNE-GLPs).

Figure 2 .
Figure 2. Coal-fired power electricity and associated coal consumption for power generation, and the emissions of NOx, SO 2 , and PM 2.5 from thermal power plants from 2000 to 2015 in China.

Figure 3 .
Figure 3.The horizontal domain of the model (WRF-CHEM), with the location of sampling sites (shown by the green crosses), and topographical conditions of the NCP, which are surrounded by the Mountains of Yan and Tai in the north and west, respectively.

Figure 8 .
Figure 8.The lower (left panels) and upper (right panels) episode-averaged variations induced by GLP, including the mass concentrations (µg m -3 ) of (a) PM 2.5 , (b) NO 2 , and (c) SO 2 .The results refer to the spatial variations between the REF case and the SEN-GLPs case (REF − SNE-GLPs).The red-squares display the areas with high PM 2.5 changes induced by the GLP.