Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network

Scopes and databases

The CEAP dataset comprises all the thermal power plants operating in China, totalling 2,714 plants (or 6,267 units), from 2014 to 2017 in 26 provinces and 4 municipalities (except Hong Kong, Macao, Taiwan and Tibet; Table 1). The thermal power plants produce electricity by combusting a variety of fossil energies, which fall into 4 categories: coal, gas plus oil, biomass and others (detailed in Table 2).

The CEAP dataset integrates two databases, i.e., the CEMS data and unit-specific information. The CEMS data—the direct, real-time measurements of stack gas concentrations of PM, SO₂ and NO_X from China’s power plant stacks—are monitored by China’s CEMS network and reported to the China Ministry of Ecology and Environment (MEE; http://www.envsc.cn/). The CEMS data are recorded on a source and hourly basis. In total, the CEMS dataset covers 4,622 emission sources (i.e., power plant stacks) associated with 5,606 units (accounting for 98% of China’s thermal power capacity), 35,064 hours from 2014 to 2017, and 3 air pollutants (i.e., PM, SO₂ and NO_X) for each source-hour sample (Table 3). The MEE has also provided stack-specific information (regarding latitude and longitude, heights, temperature, diameter, etc.; http://permit.mee.gov.cn/).

Unit-specific information is also derived from the MEE, involving activity levels (energy consumption and power generation), operating capacities, geographic allocations and pollution control equipment (particularly the types and removal efficiencies) at a yearly frequency. Due to data availability, the unit information is available only until 2016, and the activity levels for 2017 are projected following the overall trends in provincial thermal power generation between 2016 and 2017 (which are available in the China Energy Statistical Yearbooks²⁶), under the assumption that new units constructed in 2017 have the same structures of installed capacities, energy uses and regions as those of the existing units in 2016.

With a combination of the two datasets, the CEAP dataset provides nationwide, plant-level, dynamic PM, SO₂ and NO_X emissions from China’s thermal power plants from 2014 to 2017. Relative to existing inventories, the CEAP dataset is innovative in that it incorporates comprehensive real CEMS-measured emission data, avoiding the use of average emission factors and the associated operational assumptions and uncertain parameters.

Pre-processing of CEMS data

We have been exclusively granted access to the data from China’s CEMS network. Generally, the CEMS consists of a sampling system (for filtering and sampling flue gas), an online analytical component (for monitoring flue gas parameters, particularly emission concentrations) and a data processing system (for collecting, processing and reporting monitoring data)^27,28. According to the GB13223-2003 regulation²⁹, the CEMS network should cover all power plant furnaces that burn coal (except stoker and spreader stoker) and oil and generate >65 tons of steam each hour, as well as those that burn pulverized coal and gas. Thus, some power plants have not yet been incorporated into the CEMS network (accounting for 3–4% of the total thermal power capacity from 2014 to 2017) because their furnaces did not meet the requirements necessary to install a CEMS. For the power plants outside the CEMS network, we assume their stack concentrations are similar to the averages of the units with similar fuel types and similar regions within the CEMS network.

To guarantee the reliability of CEMS data, China’s government has made great efforts in developing specific regulations and technical guidelines for power plants and local entities to follow and supervise, respectively^{24,28,30,31,32}. These official documents elaborate on all the processes required to regulate the CEMS network, including not only CEMS installation, operation, inspection, maintenance and repair but also CEMS data collection, processing, reporting, analysis and storage^28,32,33. Since 2014, all state-monitored companies have been mandated to report their CEMS data to the local governments through a series of online platforms for different provinces (listed in Supplementary Table 1). Local entities have random onsite inspections to check the truthfulness of the reported results on at least a quarterly basis^{23,24,28,32,34}; this system enables a comparison of CEMS data across different firms to explore potential outliers and abnormalities and prevent data manipulation^28,35. Then, the governments release the inspection results to the public through the same online platforms (listed in Supplementary Table 1)^24,36,37. Severe financial penalties and criminal punishments can be imposed on firms that adopt data manipulation (in terms of deleting, distorting and forging CEMS data, for example)^38,39.

The malfunction of CEMS monitors may also introduce large uncertainty to CEMS data during the processes of operation (indication errors, span drift, zero drift, etc.), maintenance (particularly the failure to perform calibration and reference tests) and data reporting (invalid data communication, data missing, etc.)^24,28. Accordingly, each power plant is required to make at least one A-, B- and C-grade overhaul for 32–80, 14–50 and 9–30 days per 4–6, 2–3 and 1 year(s), respectively, as well as one D-grade overhaul (if needed) for 5–15 days per year, to check, maintain and upgrade its technologies, thereby reducing measurement uncertainty⁴⁰. During these overhauls, CEMS operators conduct CEMS calibration (i.e., zero and span calibration), maintenance procedures (e.g., examining and cleaning major CEMS components and replacing or upgrading parts, if necessary, such as optical lens, filter and sampling meter) and a reference test (i.e., relative accuracy test audit). Furthermore, third-party operators examine CEMS operation and maintenance routines, to guarantee standardized CEMS operation and facilitate improvement in CEMS data accuracy^27,28,31. All the related activities should be documented according to standardized requirement contents^27,28. Even with the aforementioned efforts, there is still a small proportion of nulls and outliers in the CEMS database, which represent 1% and 0.1% of the total operating hours, respectively, from 2014 to 2017. We treat these samples seriously by following the relevant official documents, which have been released by China’s government. Table 4 provides the treatment methods for nulls or zeros, which can be divided into 3 types based on duration. On the one hand, we consider nulls and/or zeros that span at least 5 successive days as a downtime or overhaul and omit them in the estimation, according to the regulation²⁷. On the other hand, missing data lasting < 5 day(s) are treated as outliers (i.e., impossible values in operation) and processed in two different ways: the nulls and/or zeros successive for > 24 hours are assumed around the valid values near the time and set to the monthly averages²⁷:

where ({C}_{s,i,y,m,h}) denotes the stack gas concentrations of pollutant s emitted by unit i for year y, month m and hour h (i.e., the actual measurements monitored by the CEMS network), defined as the amount of pollutants per unit of emitted stack gas (g m⁻³)^41,42; ({widehat{C}}_{s,i,y,m,h}) is the imputation for the missing data ({C}_{s,i,y,m,h}); ({bar{C}}_{s,i,y,m,.}) is the mean of the hourly valid values for the same pollutant, unit, year and month as ({C}_{s,i,y,m,h}). In contrast, the missing data for 1–24 hour(s) are interpolated with the arithmetic averages of the two nearest valid points before and after them^27,43:

where ({C}_{s,i,y,m,h-l}) and ({C}_{s,i,y,m,h{rm{+}}q}) represent the nearest last known measurements (l hour(s) before) and next known measurements (q hour(s) after), respectively, for the missing data ({C}_{s,i,y,m,h}), namely, the series data ({C}_{s,i,y,m,h-l+1}),…, ({C}_{s,i,y,m,h}),…, ({C}_{s,i,y,m,h+q-1}) are all missing values. Furthermore, we treat the measurements that are out of the measurement ranges of CEMS instruments (outside of which the data are unreliable^30,44; detailed in Supplementary Table 2) as abnormal data and process them in a similar way to nulls according to the official regulation²⁷.

CEMS-based estimation of emission factors and absolute emissions

The introduction of real CEMS-monitored measurements provides a direct estimation for emission factors on a source and hourly basis, avoiding the use of average emission factors with many assumptions and uncertain parameters^17,42,44.

In Eq. (3), (E{F}_{s,i,y,m,h}) indicates the emission factor, defined as the amount of emissions per unit of fuel use (in g kg⁻¹ for solid or liquid fuel and in g m⁻³ for gas fuel), and ({V}_{i,y}) is the theoretical flue gas rate, defined as the expected volume of flue gas per unit of fuel use under standard production conditions (m³ kg⁻¹ for solid or liquid fuel and m³ m⁻³ for gas fuel)⁴², which was estimated by the China Pollution Source Census (2011)⁴⁵ based on sufficient field measurements (detailed in Table 5). Based on Eq. (3), abated emission factors can be directly obtained even without the use of removal efficiencies and the relevant parameters, because CEMS monitors the gas concentrations at stacks after the effect of control equipment (if any).

Notably, recent clean air policies (particularly different emissions standards) target stack concentrations, such that a large proportion of missing data exist regarding other measurements (particularly flue gas rates, with missing data accounting for 34.62%, 31.91%, 29.97% and 42.96% of the total samples in 2014, 2015, 2016 and 2017, respectively). Accordingly, we introduce theoretical flue gas rates into the estimation to avoid significant underestimation of the actual volume when there are too many missing data values⁴⁶. In addition, the adoption of theoretical flue gas rates can address flue gas leakage, a common problem in power plants that greatly distorts the real flue gas volume⁴⁶. The theoretical flue gas rates are derived from the China Pollution Source Census, with values varying across operating capacities, fuel types and boiler types^42,45. Thus, the actual volume of flue gas is computed in terms of the theoretical flue gas rate times actual fuel consumption.

The absolute emissions of PM, SO₂ and NO_X from individual power plants can be estimated in terms of the emission factors times the activity levels²¹:

where ({E}_{s,i,y,m}) represents the air pollution emissions (g); and ({A}_{i,y,m}) is the activity data, i.e., the amount of fuel use (kg for solid or liquid fuel and m³ for gas fuel). In the CEAP dataset, power plant emissions are estimated on a monthly basis (the smallest scale for activity data), in which the yearly unit-level activity data are allocated at the monthly scale using the monthly province-level thermal power generation as weights¹⁶:

where ({F}_{{p}_{i},y,m}) denotes the thermal power generation by province P_i, which is obtained from the Chinese Energy Statistics Yearbook²⁶, and ({p}_{i}) indicates the province where unit i is located.

Source: Ecology - nature.com