Abstract. China has been conducting a series of actions on air quality improvement for
the past decades, and air pollutant emissions have been changing swiftly
across the country. Provinces are an important administrative unit for air
quality management in China; thus a reliable provincial-level emission
inventory for multiple years is essential for detecting the varying sources
of pollution and evaluating the effectiveness of emission controls. In this
study, we selected Jiangsu, one of the most developed provinces in China,
and developed a high-resolution emission inventory of nine species for
2015–2019, with improved methodologies for different emission sectors, best
available facility-level information on individual sources, and real-world
emission measurements. Resulting from implementation of strict emission
control measures, the anthropogenic emissions were estimated to have
declined 53 %, 20 %, 7 %, 2 %, 10 %, 21 %, 16 %, 6 %, and
18 % for sulfur dioxide (SO2), nitrogen oxides (NOx), carbon
monoxide (CO), non-methane volatile organic compounds (NMVOCs), ammonia
(NH3), inhalable particulate matter (PM10), fine particulate
matter (PM2.5), black carbon (BC), and organic carbon (OC) from 2015 to
2019, respectively. Larger abatement of SO2, NOx, and PM2.5
emissions was detected for the more developed region of southern Jiangsu. During the period from 2016 to 2019,
the ratio of biogenic volatile organic compounds (BVOCs) to anthropogenic
volatile organic compounds (AVOCs) exceeded 50 % in the month of July, indicating the
importance of biogenic sources for summer O3 formation. Our estimates in
annual emissions of NOx, NMVOCs, and NH3 were generally smaller
than the national emission inventory, MEIC (the Multi-resolution Emission
Inventory for China), but larger for primary particles.
The discrepancies between studies resulted mainly from different methods of
emission estimation (e.g., the procedure-based approach for AVOC emissions
from key industries used in this work) and inconsistent information of
emission source operation (e.g., the penetration and removal efficiencies
of air pollution control devices). Regarding the different periods, more
reduction of SO2 emissions was found between 2015 and 2017 and of
NOx, AVOCs, and PM2.5 between 2017 and 2019. Among the selected
13 major measures, the ultra-low-emission retrofit in the power sector was the
most important contributor to the reduced SO2 and NOx emissions
(accounting for 38 % and 43 % of the emission abatement, respectively)
for 2015–2017, but its effect became very limited afterwards as the retrofit
had been commonly completed by 2017. Instead, extensive management of
coal-fired boilers and the upgrade and renovation of non-electrical industry
were the most important measures for 2017–2019, accounting collectively for
61 %, 49 %, and 57 % reduction of SO2, NOx, and PM2.5,
respectively. Controls on key industrial sectors were the most
effective for AVOC reduction in the two periods, while measures relating to other
sources (transportation and solvent replacement) have become more important in
recent years. Our provincial emission inventory was demonstrated to
support high-resolution air quality modeling for multiple years.
Through scenario setting and modeling, worsened meteorological conditions
were found from 2015 to 2019 for PM2.5 and O3 pollution
alleviation. However, the efforts on emission controls were identified to
largely overcome the negative influence of meteorological variation. The
changed anthropogenic emissions were estimated to contribute 4.3 and 5.5 µg m−3 of PM2.5 concentration reduction for
2015–2017 and 2017–2019, respectively. While O3 was elevated by 4.9 µg m−3 for 2015–2017, the changing emissions led to 3.1 µg m−3 of reduction for 2017–2019, partly (not fully though)
offsetting the meteorology-driven growth. The analysis justified the
validity of local emission control efforts on air quality improvement and
provided a scientific basis to formulate air pollution prevention and control
policies for other developed regions in China and worldwide.
