我国典型区域二氧化碳和细颗粒物精细化协同减排路径

随着碳达峰、碳中和目标的提出,我国环境保护进入减污降碳协同治理新阶段.目前研究主要探寻空气质量政策或气候政策对二氧化碳和细颗粒物的减排效益.而从二氧化碳和细颗粒物排放终端能源消费重点行业、主要能源以及主导因素等角度出发,来探究我国典型区域协同减排路径精细化方法的研究则鲜见报道.本研究基于2000~2020年我国京津冀、长江三角洲、珠江三角洲典型区域能源消耗数据,首先探明了3个典型区域二氧化碳和细颗粒物终端能源消费的重点排放行业均为工业;并进一步甄别了终端能源消费重点行业中PM_(2.5)排放主要能源为煤类能源,而CO_(2)排放主要能源由煤类能源逐渐转向煤、气类能源,但煤类仍为主导地位;随后使用因素分解模型解析了能源强度、技术进步等主导因素对单位国内生产总值的二氧化碳以及细颗粒物的影响效应;最终利用能源-环境核算预测模型,基于上述研究识别的终端能源消费重点行业、主要能源以及主导因素进行情景分析,旨在判断典型区域不同情景下碳达峰情况,进而寻找协同减排最优路径.结果发现,“技术进步”因素在前期减排效果最好;“能效提升”因素的减排效果在长时期碳污协同减排将起到关键作用,表明未来针对工业排放等终端能源消费重点行业,关注煤、气类等主要能源,采取能效提升、技术进步等手段措施,能够对减污降碳协同增效目标的实现产生最佳收益.希望本研究能为我国典型区域二氧化碳和细颗粒物合理化、精细化协同减排和管控提供科学证据.

Impacts of meteorology and precursor emission change on O_(3) variation in Tianjin, China from 2015 to 2021

Deterioration of surface ozone (O_(3)) pollution in Northern China over the past few years received much attention.For many cities,it is still under debate whether the trend of surface O_(3)variation is driven by meteorology or the change in precursors emissions.In this work,a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O_(3)trend and identify the key meteorological factors affecting O_(3)pollution in Tianjin,the biggest coastal port city in Northern China.After “removing” the meteorological fluctuations from the observed O_(3)time series,we found that variation of O_(3)in Tianjin was largely driven by the changes in precursors emissions.The meteorology was unfavorable for O_(3)pollution in period of 2015-2016,and turned out to be favorable during 2017-2021.Specifically,meteorology contributed 9.3μg/m^(3)O_(3)(13%) in 2019,together with the increase in precursors emissions,making 2019 to be the worst year of O_(3)pollution since 2015.Since then,the favorable effects of meteorology on O_(3)pollution tended to be weaker.Temperature was the most important factor affecting O_(3)level,followed by air humidity in O_(3)pollution season.In the midday of summer days,O_(3)pollution frequently exceeded the standard level (>160μg/m^(3)) at a combined condition with relative humidity in 40%-50%and temperature>31℃.Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O_(3)forecasting.

Quantifying the impacts of emissions and meteorology on the interannual variations of air pollutants in major Chinese cities from 2015 to 2021

Air pollutant concentration is a function of emission rates and meteorology.To accurately evaluate the effect of control measures,the meteorological covariate must be corrected from the observations.This study quantified the impacts of emission abatement and meteorological condition on the interannual variations of SO_(2),NO_(2),CO,O_(3),PM_(10) and PM_(2.5) concentrations in 31 major Chinese cities using an optimized machine learning-based meteorological normalization technique.Overall,the annual average concentrations of SO_(2),NO_(2),CO,PM_(10)and PM_(2.5)were reduced by 86%,51%,99%,86% and 88%from 2015 to 2020,respectively,in the studied cities,attributable to their emission reductions.However,the concentration of O_(3) was found with no significant decrease with the reduction of precursors.Emission abatement notably improved air quality between 2015 and 2018.Such a decline in emissions tended to progressively slow down since 2018.Overall,the meteorological conditions in 2016–2017 and 2018–2019 were unfavorable for a better air quality,while it became favorable in 2020–2021.Specifically,emission abatement in 2021 further lowered the concentrations of SO_(2),NO_(2),CO,and PM_(2.5),while the emission of PM_(10) increased.And changes in precursors emissions worsened O_(3) air quality.To meet the demand of improving air quality,more aggressive abatement measures need to be formulated to synergistically reduce NOx,volatile organic compounds,and coarse particles.

New insights into the formation of ammonium nitrate from a physical and chemical level perspective

High levels of fine particulate matter(PM_(2.5))is linked to poor air quality and premature deaths,so haze pollution deserves the attention of the world.As abundant inorganic components in PM_(2.5),ammonium nitrate(NH_(4)NO_(3))formation includes two processes,the diffusion process(molecule of ammonia and nitric acid move from gas phase to liquid phase)and the ionization process(subsequent dissociation to form ions).In this study,we discuss the impact of meteorological factors,emission sources,and gaseous precursors on NH4NO3 formation based on thermodynamic theory,and identify the dominant factors during clean periods and haze periods.Results show that aerosol liquid water content has a more significant effect on ammonium nitrate formation regardless of the severity of pollution.The dust source is dominant emission source in clean periods;while a combination of coal combustion and vehicle exhaust sources is more important in haze periods.And the control of ammonia emission is more effective in reducing the formation of ammonium nitrate.The findings of this work inform the design of effective strategies to control particulate matter pollution.

Corrigendum to “Drivers of PM_(2.5)-O_(3) co-pollution: from the perspective of reactive nitrogen conversion pathways in atmospheric nitrogen cycling” [Sci. Bull. 67(18) (2022) 1833–1836]

The authors would like to correct Fig.1e,f.Due to our neglect when doing the picture layout of Fig.1,the abscissa in Fig.1e,f is error:the abscissa ranges from
80 to 0 in Fig.1e and ranges from
90 to
20 in Fig.1f.The image has been corrected:the abscissa ranges from
80 to 80 in Fig.1e and ranges from
80 to 80 in Fig.1f[1].We declare that this correction does not change the results or conclusions of this paper.

Vertical characteristics and source identification of PM_(10) in Tianjin

Ambient PM 10 （particulate matter with a diameter less than 10μm） concentrations were measured on a 255 meter tower in Tianjin,China.The samples were collected at four vertical levels （10,40,120 and 220 m）.Vertical characteristics for PM 10 samples were studied.The results showed that the concentrations of PM 10 and constituent species had a negative correlation with the sampling height.The highest concentrations of PM 10 and species were obtained at the 10 m level,and the lowest concentrations were measured at the 220 m level.For the fractions of species to total mass,SO 4 2- and NO 3- had higher values （fraction） at greater height;while Ca had a higher fraction at lower height.Possible source categories for the PM 10 ambient dataset were identified by the principal component analysis method.The possible source categories included crustal dust,vehicles,cement dust,and incineration as well as secondary sulfate and nitrate sources.Analysis of meteorological factors on PM 10 concentrations indicated that wind speed and inversion may be the main factors contributing to different concentrations of PM 10 at different heights.

Chemical characterization and source apportionment of PM_(1) and PM_(2.5) in Tianjin, China: Impacts of biomass burning and primary biogenic sources

The submicron particulate matter(PM_(1))and fine particulate matter(PM_(2.5))are very important due to their greater adverse impacts on the natural environment and human health.In this study,the daily PM_(1) and PM_(2.5) samples were collected during early summer 2018 at a sub-urban site in the urban-industrial port city of Tianjin,China.The collected samples were analyzed for the carbonaceous fractions,inorganic ions,elemental species,and specific marker sugar species.The chemical characterization of PM_(1) and PM_(2.5) was based on their concentrations,compositions,and characteristic ratios(PM_(1)/PM_(2.5),AE/CE,NO3^-/SO4^2-,OC/EC,SOC/OC,OM/TCA,K^+/EC,levoglucosan/K^+,V/Cu,and V/Ni).The average concentrations of PM_(1) and PM_(2.5) were 32.4μg/m and 53.3μg/m^3,and PM_(1) constituted 63%of PM_(2.5) on average.The source apportionment of PM_(1) and PM_(2.5) by positive matrix factorization(PMF)model indicated the main sources of secondary aerosols(25%and 34%),biomass burning(17%and 20%),traffic emission(20%and 14%),and coal combustion(17%and 14%).The biomass burning factor involved agricultural fertilization and waste incineration.The biomass burning and primary biogenic contributions were determined by specific marker sugar species.The anthropogenic sources(combustion,secondary particle formation,etc)contributed significantly to PM_(1) and PM_(2.5),and the natural sources were more evident in PM_(2.5).This work significantly contributes to the chemical characterization and source apportionment of PM_(1) and PM_(2.5) in near-port cities influenced by the diverse sources.

Assessment of heavy metal pollution characteristics and human health risk of exposure to ambient PM_(2.5) in Tianjin,China

To examine the features of heavy metal pollution of PM2.5 （particulate matter less than 2.5 μm） in Tianjin, China, as well as the exposure risk of PM2.5 to human health, we analyzed ambient PM2.5samples collected from a campus of Nankai University in June, August, and October 2012. The concentrations of PM2.5 and heavy metals （Ni, Cu, Pb, Zn, Cr, Cd, Hg, As and Mn） in PM2.5 were analyzed by gravimetric analysis and inductively coupled plasma-mass spectrometry, respectively. The results show that the heavy metals contained in PM2.5 were, in descending order, Cu, Zn, Pb, Mn, Cr, Ni, Cd, As, and Hg. The proportion of Cd exceeded the secondary level of National Ambient Air Quality Standard of China （GB 3095-2012） by 1.3 times, while others were within the limit. Enrichment factor analysis indicated that Cu, Zn, Cd, Pb, and Hg are mainly from anthropogenic sources. Principal component analysis indicated that the main sources of the heavy metals are vehicle exhaust, chemical waste, and coal-burning activities. The nine heavy metals which may cause health issues by exposure through the human respiratory system and should be further examined are Cr, Cd, As, Ni, Cu, Pb, Mn, Zn, and Hg, in the order of decreasing risk levels. With reference to the U.S. EPA standard the risk levels of all nine metals were below the acceptable level （10 6/year）.

Size distribution and chemical characteristics of particles from crop residue open burning in North China

Crop residue open burning is an important emission source of ambient particles in China.This study analyzed the particle emission characteristics of crop residue open burning through combustion experiments with a novel open combustion simulation device using three typical crop straws in north China(corn,wheat,and rice).Particle samples size ranging from 0.006–9.890μm were collected by an Electrical Low Pressure Impactor plus,a high size-resolution instrument capable of dividing particles into 14 size stages.The size distributions of organic carbon(OC),elemental carbon(EC),water-soluble ions,and elements were analyzed,and source chemical profiles were constructed for PM0.1,PM1,PM2.5,and PM10.The number concentration of particles was concentrated in the Aiken nuclei mode(0.006–0.054μm),accounting for 75%of the total number,whereas the mass concentration was concentrated in the accumulation mode(0.054–0.949μm),accounting for 85.43%of the mass loading.OC,EC,Cl−,and K(include total K and water-soluble K)were the major chemical components of the particles,whose mass percentage distributions differed from those of other components.These fivemain components exhibited a bell-shaped size distribution in the 0.006–9.890μm range,whereas the other components exhibited a U-shaped distribution.Among the chemical profiles for PM0.1–PM10,OC was the most important component at 10–30%,followed by EC at 2%–8%.The proportions of K^(+),Cl^(−),and K varied substantially in different experimental groups,ranging from 0–15%,and K+and Cl−were significantly correlated(r=0.878,α=0.000).

Impact of meteorological condition changes on air quality and particulate chemical composition during the COVID-19 lockdown

Stringent quarantine measures during the Coronavirus Disease 2019(COVID-19)lockdown period(January 23,2020 to March 15,2020)have resulted in a distinct decrease in anthropogenic source emissions in North China Plain compared to the paralleled period of 2019.Particularly,22.7%decrease in NO_(2)and 3.0%increase of O_(3)was observed in Tianjin,nonlinear relationship between O_(3)generation and NO_(2)implied that synergetic control of NOx and VOCs is needed.Deteriorating meteorological condition during the COVID-19 lockdown obscured the actual PM2.5 reduction.Fireworks transport in 2020 Spring Festival(SF)triggered regional haze pollution.PM2.5 during the COVID-19 lockdown only reduced by 5.6%in Tianjin.Here we used the dispersion coefficient to normalize the measured PM2.5(DN-PM2.5),aiming to eliminate the adverse meteorological impact and roughly estimate the actual PM2.5 reduction,which reduced by 17.7%during the COVID-19 lockdown.In terms of PM2.5 chemical composition,significant NO_(3)−increase was observed during the COVID-19 lockdown.However,as a tracer of atmospheric oxidation capacity,odd oxygen(Ox=NO_(2)+O_(3))was observed to reduce during the COVID-19 lockdown,whereas relative humidity(RH),specific humidity and aerosol liquid water content(ALWC)were observed with noticeable enhancement.Nitrogen oxidation rate(NOR)was observed to increase at higher specific humidity and ALWC,especially in the haze episode occurred during 2020SF,high air humidity and obvious nitrate generation was observed.Anomalously enhanced air humidity may response for the nitrate increase during the COVID-19 lockdown period.

Local and long-range transport influences on PM_(2.5) at a cities-cluster in northern China,during summer 2008

Hourly PM2.5 concentrations were observed simultaneously at a cities-cluster comprising 10 cities/towns in Hebei province in China from July 1 to 31, 2008. Among the 10 cities/towns, Baoding showed the high- est average concentration level （161.57μg/m3） and Yanjiao exhibited the lowest （99.35 μg/m3 ）. These observed data were also studied using the joint potential source contribution function with 24-h and 72-h backward trajectories, to identify more clearly the local and countrywide-scale long-range transport sources. For the local sources, three important influential areas were found, whereas five important influential areas were defined for long-range transport sources. Spatial characteristics of PM2.5 were determined by multivariate statistical analyses. Soil dust, coal combustion, and vehicle emissions might be the potential contributors in these areas. The results of a hierarchical cluster analysis for back trajectory endpoints and PM2.s concentrations datasets show that the spatial characteristics of PM2.5 in the cities-cluster were influenced not only by local sources, but also by long-range transport sources. Different cities in the cities-cluster obtained different weighted contributions from local or long-range transport sources. Cangzhou, Shijiazhuang, and Baoding are near the source areas in the south of Hebei province, whereas Zhuozhou, Yangfang, Yanjiao, Xianghe, and Langfang are close to the sources areas near Beijing and Tianjin.

Insight into the critical factors determining the particle number concentrations during summer at a megacity in China

To identify the critical factors impacting the number concentration of particles with the aerodynamic diameters less than 2.5 μm(PNC_(2.5)), the continuous measurement of PNC_(2.5),chemical components in PM_(2.5), gaseous pollutants and meteorological conditions were conducted at an urban site in Tianjin in June 2015. Results indicated that the average PNC_(2.5) was 2839 ± 2430 d N/dlog Dp 1/cm^3 during the campaign. Compared to other meteorological parameters, the relative humidity(RH) had the strongest relationship with PNC_(2.5), with a Pearson's correlation coefficient of 0.53, and RH larger than 30% influenced strongly PNC_(2.5).The important influence of secondary reactions on PNC_(2.5) was inferred due to higher correlation coefficients between PNC_(2.5) and SO_4^(2-), NO_3^-, NH_4^+(r = 0.78–0.89; p < 0.01) and between PNC_(2.5) and ratios that represent the conversion of nitrogen and sulfur oxides to particulate matter(r = 0.42–0.49; p < 0.01). Under specific RH conditions, there were even stronger correlations between PNC_(2.5) and NO_3^-, SO_4^(2-), NH_4^+, while those between PNC_(2.5) and EC, OC were relatively weak, especially when RH exceeded 50%. Principal component analysis(PCA) and Pearson's correlation analysis indicated that secondary sources, vehicle emission and coal combustion might be major contributors to PNC_(2.5). Backward trajectory and potential source contribution function(PSCF) analysis suggested that the transport of air masses originated from these regions around Tianjin(Liaoning, Hebei, Shandong and Jiangsu) influenced critically PNC_(2.5). The north of Jiangsu, the west of Shandong, and the east of Hebei were distinguished as major potential source-areas of PNC_(2.5) by PSCF model.

Machine learning and theoretical analysis release the non-linear relationship among ozone,secondary organic aerosol and volatile organic compounds

Fine particulate matter(PM_(2.5))and ozone(O_(3))pollutions are prevalent air quality issues in China.Volatile organic compounds(VOCs)have significant impact on the formation of O_(3)and secondary organic aerosols(SOA)contributing PM_(2.5).Herein,we investigated 54 VOCs,O_(3)and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O_(3),SOA and VOCs.The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September,but the observed O_(3)was exactly the opposite.Machine learning methods resolved the importance of individual VOCs on O_(3)and SOA that alkenes(mainly ethylene,propylene,and isoprene)have the highest importance to O_(3)formation;alkanes(C_(n),n≥6)and aromatics were the main source of SOA formation.Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O_(3)and SOA formation.Ozone formation potential(OFP)and secondary organic aerosol formation potential(SOAFP)calculated by consumed VOCs quantitatively indicated that more than 80%of the consumed VOCs were alkenes which dominated the O_(3)formation,and the importance of consumed aromatics and alkenes to SOAFP were 40.84%and 56.65%,respectively.Therein,isoprene contributed the most to OFP at 41.45%regardless of the season,while aromatics(58.27%)contributed the most to SOAFP in winter.Collectively,our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O_(3).

从大气氮循环中活性氮转化途径的角度谈PM_(2.5)-O_(3)复合污染的驱动因素

Fine particulate matter(PM)and ozone(O),two globally signifcant air pollutants,have exerted substantial adverse impacts on climate and human health[1].From 2013 to 2020,China has achieved a signifcant decline of PMlevels,though O3pollution has deteriorated over time[2].PM-Oco-pollution includes not only both high levels of PMand O,but also high PMor Oeven when the other remain low.Therefore,the coordinated control of PMand Oshould not only focus on reducing high concentrations of PMand Osimultaneously.