Chemical characterization and source identification of PM2.5 in Luoyang after the clean air actions

Luoyang is a typical heavy industrial city in China, with a coal-dominated energy structure and serious air pollution. Following the implementation of the clean air actions, the physicochemical characteristics and sources of PM_2.5 have changed. A comprehensive study of PM_2.5 was conducted from October 16, 2019 to January 23, 2020 to evaluate the effectiveness of previous control measures and further to provide theory basis for more effective policies in the future. Results showed that the aerosol pollution in Luoyang in autumn and winter is still serious with the average concentration of 91.1 μg/m³, although a large reduction (46.9%) since 2014. With the contribution of nitrate increased from 12.5% to 25.1% and sulfate decreased from 16.7% to 11.2%, aerosol pollution has changed from sulfate-dominate to nitrate-dominate. High NO₃⁻/SO₄²⁻ ratio and the increasing of NO₃⁻/SO₄²⁻ ratio with the aggravation of pollution indicating vehicle exhaust playing an increasingly important role in PM_2.5 pollution in Luoyang, especially in the haze processes. Secondary inorganic ions contributed significantly to the enhancement of PM_2.5 during the pollution period. The high value of Cl⁻/Na⁺ and EC concentration indicate coal combustion in Luoyang is still serious. The top three contributor sources were secondary inorganic aerosols (33.3%), coal combustion (13.6%), and industrial emissions (13.4%). Close-range transport from the western and northeastern directions were more important factors in air pollution in Luoyang during the sampling period. It is necessary to strengthen the control of coal combustion and reduce vehicle emissions in future policies.     

Chemical formation and source apportionment of PM2.5 at an urban site at the southern foot of the Taihang mountains

The region along the Taihang Mountains in the North China Plain (NCP) is characterized by serious fine particle pollution. To clarify the formation mechanism and controlling factors, an observational study was conducted to investigate the physical and chemical properties of the fine particulate matter in Jiaozuo city, China. Mass concentrations of the water-soluble ions (WSIs) in PM_2.5 and gaseous pollutant precursors were measured on an hourly basis from December 1, 2017, to February 27, 2018. The positive matrix factorization (PMF) method and the FLEXible PARTicle (FLEXPART) model were employed to identify the sources of PM_2.5. The results showed that the average mass concentration of PM_2.5 was 111 μg/m³ during the observation period. Among the major WSIs, sulfate, nitrate, and ammonium (SNA) constituted 62% of the total PM_2.5 mass, and NO₃^? ranked the highest with an average contribution of 24.6%. NH₄⁺ was abundant in most cases in Jiaozuo. According to chemical balance analysis, SO₄^2?, NO₃^?, and Cl^? might be present in the form of (NH₄)₂SO₄, NH₄NO₃, NH₄Cl, and KCl. The liquid-phase oxidation of SO₂ and NO₂ was severe during the haze period. The relative humidity and pH were the key factors influencing SO₄^2- formation. We found that NO₃^? mainly stemmed from homogeneous gas-phase reactions in the daytime and originated from the hydrolysis of N₂O₅ in the nighttime, which was inconsistent with previous studies. The PMF model identified five sources of PM_2.5: secondary origin (37.8%), vehicular emissions (34.7%), biomass burning (11.5%), coal combustion (9.4%), and crustal dust (6.6%).     

Chemical characteristics and source apportionment of PM2.5 in a petrochemical city: Implications for primary and secondary carbonaceous component

To study the pollution features and underlying mechanism of PM_2.5 in Luoyang, a typical developing urban site in the central plain of China, 303 PM_2.5 samples were collected from April 16 to December 29, 2015 to analyze the elements, water soluble inorganic ions, organic carbon and elemental carbon. The annual mean concentration of PM_2.5 was 142.3 μg/m³, and 75% of the daily PM_2.5 concentrations exceeded the 75 μg/m³. The secondary inorganic ions, organic matter and mineral dust were the most abundant species, accounting for 39.6%, 19.2% and 9.3% of the total mass concentration, respectively. But the major chemical components showed clear seasonal dependence. SO₄^2? was most abundant specie in spring and summer, which related to intensive photochemical reaction under high O₃ concentration. In contrast, the secondary organic carbon and ammonium while primary organic carbon and ammonium significantly contributed to haze formation in autumn and winter, respectively. This indicated that the collaboration effect of secondary inorganic aerosols and carbonaceous matters result in heavy haze in autumn and winter. Six main sources were identified by positive matrix factorization model: industrial emission, combustion sources, traffic emission, mineral dust, oil combustion and secondary sulfate, with the annual contribution of 24%, 20%, 24%, 4%, 5% and 23%, respectively. The potential source contribution function analysis pointed that the contribution of the local and short-range regional transportation had significant impact. This result highlighted that local primary carbonaceous and precursor of secondary carbonaceous mitigation would be key to reduce PM_2.5 and O₃ during heavy haze episodes in winter and autumn.     

Cross-boundary transport and source apportionment for PM2.5 in a typical industrial city in the Hebei Province, China: A modeling study

Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain. In this study, an industrial district (Shahe City) with a large glass manufacturing sector was investigated to clarify the relative contribution of fine particulate matter (PM_2.5) to the city's high levels of pollution. The Nest Air Quality Prediction Model System (NAQPMS), paired with Weather Research and Forecasting (WRF), was adopted and applied with a spatial resolution of 5 km. During the study period, the mean mass concentrations of PM_2.5, SO₂, and NO₂ were observed to be 132.0, 76.1, and 55.5 μg/m³, respectively. The model reproduced the variations in pollutant concentrations in Shahe at an acceptable level. The simulation of online source-tagging revealed that pollutants emitted within a 50-km radius of downtown Shahe contributed 63.4% of the city's total PM_2.5 concentration. This contribution increased to 73.9±21.2% when unfavorable meteorological conditions (high relative humidity, weak wind, and low planetary boundary layer height) were present; such conditions are more frequently associated with severe pollution (PM_2.5 ≥ 250 μg/m³). The contribution from Shahe was 52.3±21.6%. The source apportionment results showed that industry (47%), transportation (10%), power (17%), and residential (26%) sectors were the most important sources of PM_2.5 in Shahe. The glass factories (where chimney stack heights were normally < 70 m) in Shahe contributed 32.1% of the total PM_2.5 concentration in Shahe. With an increase in PM_2.5 concentration, the emissions from glass factories accumulated vertically and narrowed horizontally. At times when pollution levels were severe, the horizontally influenced area mainly covered Shahe. Furthermore, sensitivity tests indicated that reducing emissions by 20%, 40%, and 60% could lead to a decrease in the mass concentration of PM_2.5 of of 12.0%, 23.8%, and 35.5%, respectively.     

Fine particulate matter (PM2.5/PM1.0) in Beijing, China: Variations and chemical compositions as well as sources

Particulate matter (i.e., PM_1.0 and PM_2.5), considered as the key atmospheric pollutants, exerts negative effects on visibility, global climate, and human health by associated chemical compositions. However, our understanding of PM and its chemical compositions in Beijing under the current atmospheric environment is still not complete after witnessing marked alleviation during 2013–2017. Continuous measurements can be crucial for further air quality improvement by better characterizing PM pollution and chemical compositions in Beijing. Here, we conducted simultaneous measurements on PM in Beijing during 2018–2019. Results indicate that annual mean PM_1.0 and PM_2.5 concentrations were 35.49 ± 18.61 µg/m³ and 66.58 ± 60.17 µg/m³, showing a positive response to emission controls. The contribution of sulfate, nitrate, and ammonium (SNA) played an enhanced role with elevated PM loading and acted as the main contributors to pollution episodes. Discrepancies observed among chemical species between PM_1.0 and PM_2.5 in spring suggest that sand particles trend to accumulate in the range of 1–2.5 µm. Pollution episodes occurred accompanied with southerly clusters and high formation of SNA by heterogeneous reactions in summer and winter, respectively. Results from positive matrix factorization (PMF) combined with potential source contribution function (PSCF) models showed that potential areas were seasonal dependent, secondary and vehicular sources became much more important compared with previous studies in Beijing. Our study presented a continuous investigation on PM and sources origins in Beijing, which provides a better understanding for further emission control as well as a reference for other cities in developing countries.     

Temporal and spatial characteristics of PM2.5 transport fluxes of typical inland and coastal cities in China

Local pollution and the cross-boundary transmission of pollutants between cities have an inevitable impact on the atmosphere. Quantitative assessments of the contribution of transport to pollution in inland and coastal cities are necessary for the implementation of practical, regional, and joint emission control strategies. In this study, the Comprehensive Air Quality Model (CAMx), together with the Weather Research and Forecasting model (WRF), was used to simulate the contributions to pollution of different cities in 2016. The monthly inflow, outflow, and net flux from the ground to the extended layers served as the three main indicators for the analysis of the interactions of PM_2.5 transport between adjacent cities. Between inland and coastal cities, the magnitude of inflow and outflow are larger in the former than in the latter. The inflow flux in the inland cities (Beijing and Shijiazhuang) was 10.6 and 10.7 kt/day, respectively, while that in the coastal cities (Tianjin, Shanghai, Hefei, Nanjing, and Hangzhou) was 9.1, 3.3, 5.8, 4.4, and 3.7 kt/day, respectively. In terms of variation over the year, the strongest inflow in the BTH region occurred in April, followed by October, July, and January, while that in the coastal cities in YRD occurred in January, followed by October, April, and July. Therefore, based on the flux intensity calculations and the transport flux pathways, effective joint control measures can be provided with scientific support, and a better understanding of the evolutionary mechanism among inland and coastal cities can be acquired.     

Characteristics of PM2.5 pollution in Beijing after the improvement of air quality

Following the implementation of the strictest clean air policies to date in Beijing, the physicochemical characteristics and sources of PM_2.5 have changed over the past few years. To improve pollution reduction policies and subsequent air quality further, it is necessary to explore the changes in PM_2.5 over time. In this study, over one year (2017–2018) field study based on filter sampling (TH-150C; Wuhan Tianhong, China) was conducted in Fengtai District, Beijing, revealed that the annual average PM_2.5 concentration (64.8 ± 43.1 μg/m³) was significantly lower than in previous years and the highest PM_2.5 concentration occurred in spring (84.4 ± 59.9 μg/m³). Secondary nitrate was the largest source and accounted for 25.7% of the measured PM_2.5. Vehicular emission, the second largest source (17.6%), deserves more attention when considering the increase in the number of motor vehicles and its contribution to gaseous pollutants. In addition, the contribution from coal combustion to PM_2.5 decreased significantly. During weekends, the contribution from EC and NO₃^? increased whereas the contributions from SO₄^2?, OM, and trace elements decreased, compared with weekdays. During the period of residential heating, PM_2.5 mass decreased by 23.1%, compared with non-heating period, while the contributions from coal combustion and vehicular emission, and related species increased. With the aggravation of pollution, the contribution of vehicular emission and secondary sulfate increased and then decreased, while the contribution of NO₃^? and secondary nitrate continued to increase, and accounted for 34.0% and 57.5% of the PM_2.5 during the heavily polluted days, respectively.     

Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources

The submicron particulate matter (PM₁) 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 PM₁ 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 PM₁ and PM_2.5 was based on their concentrations, compositions, and characteristic ratios (PM₁/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 PM₁ and PM_2.5 were 32.4 µg/m³ and 53.3 µg/m³, and PM₁ constituted 63% of PM_2.5 on average. The source apportionment of PM₁ 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 PM₁ 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 PM₁ and PM_2.5 in near-port cities influenced by the diverse sources.     

Chemical characteristics of road dust PM2.5 fraction in oasis cities at the margin of Tarim Basin

In order to understand the compositions characteristics of particulate matter with aerodynamic diameter less than 2.5 μm(PM_2.5) fraction in road dust(RD_2.5) of oasis cities on the edge of Tarim Basin,30 road dust(RD) samples were collected in Kashi,Cele,and Yutian in the spring,2018,and RD_2.5 was collected using the resuspension approach.Eight watersoluble ions,39 trace elements and 8 fractions of carbon-containing species in PM_2.5 were analyzed.Ca     

Characteristics and source apportionment of PM2.5 under the dual influence of the Spring Festival and the COVID-19 pandemic in Yuncheng city

Based on the online and membrane sampling data of Yuncheng from January 1st to February 12th, 2020, the formation mechanism of haze under the dual influence of Spring Festival and COVID-19 (Corona Virus Disease) was analyzed. Atmospheric capacity, chemical composition, secondary transformation, source apportionment, backward trajectory, pollution space and enterprise distribution were studied. Low wind speed, high humidity and small atmospheric capacity inhibited the diffusion of air pollutants. Four severe pollution processes occurred during the period, and the pollution degree was the highest around the Spring Festival. In light, medium and heavy pollution periods, the proportion of SNA (SO₄²⁻, NO₃⁻ and NH₄⁺) was 59.6%, 56.0% and 54.9%, respectively, which was the largest components of PM_2.5; the [NO₃⁻]/[SO₄²⁻] ratio was 2.1, 1.5 and 1.7, respectively, indicating that coal source had a great influence; the changes of NOR (nitrogen oxidation ratio, 0.44, 0.45, 0.61) and SOR (sulphur oxidation ratio, 0.40, 0.49, 0.65) indicated the accumulation of secondary aerosols with increasing pollution. The coal combustion, motor vehicle, secondary inorganic sources and industrial sources contributed 36.8%, 26.59%, 11.84% and 8.02% to PM_2.5 masses, respectively. Backward trajectory showed that the influence from the east was greater during the Spring Festival, and the pollutants from the eastern air mass were higher, which would aggravate the pollution. Meteorological and Spring Festival had a great impact on heavy pollution weather. Although some work could not operate due to the impact of the COVID-19 epidemic, the emission of pollutants did not reduce much.     

Synergetic PM2.5 and O3 control strategy for the Yangtze River Delta, China

PM_2.5 concentrations have dramatically reduced in key regions of China during the period 2013–2017, while O₃ has increased. Hence there is an urgent demand to develop a synergetic regional PM_2.5 and O₃ control strategy. This study develops an emission-to-concentration response surface model and proposes a synergetic pathway for PM_2.5 and O₃ control in the Yangtze River Delta (YRD) based on the framework of the Air Benefit and Cost and Attainment Assessment System (ABaCAS). Results suggest that the regional emissions of NOx, SO₂, NH₃, VOCs (volatile organic compounds) and primary PM_2.5 should be reduced by 18%, 23%, 14%, 17% and 33% compared with 2017 to achieve 25% and 5% decreases of PM_2.5 and O₃ in 2025, and that the emission reduction ratios will need to be 50%, 26%, 28%, 28% and 55% to attain the National Ambient Air Quality Standard. To effectively reduce the O₃ pollution in the central and eastern YRD, VOCs controls need to be strengthened to reduce O₃ by 5%, and then NOx reduction should be accelerated for air quality attainment. Meanwhile, control of primary PM_2.5 emissions shall be prioritized to address the severe PM_2.5 pollution in the northern YRD. For most cities in the YRD, the VOCs emission reduction ratio should be higher than that for NOx in Spring and Autumn. NOx control should be increased in summer rather than winter when a strong VOC-limited regime occurs. Besides, regarding the emission control of industrial processes, on-road vehicle and residential sources shall be prioritized and the joint control area should be enlarged to include Shandong, Jiangxi and Hubei Province for effective O₃ control.     

Significant contribution of secondary particulate matter to recurrent air pollution: Evidence from in situ observation in the most polluted city of Fen-Wei Plain of China

Particulate matter (PM) pollution in high emission regions will affect air quality, human health and climate change on both local and regional scales, and thus attract worldwide attention. In this study, a comprehensive study on PM_2.5 and its chemical composition were performed in Yuncheng (the most polluted city of Fen-Wei Plain of China) from November 28, 2020 to January 24, 2021. The average concentration of PM_2.5 was 87.8 ± 52.0 μg/m³, which were apparently lower than those observed during the same periods of past five years, attributable to the clean air action plan implemented in this region. NO₃⁻ and organic carbon (OC) were the dominant particulate components, which on average contributed 22.6% and 16.5% to PM_2.5, respectively. The fractions of NO₃⁻, NH₄⁺, OC and trace metals increased while those of crustal materials and elemental carbon decreased with the degradation of PM_2.5 pollution. Six types of PM_2.5 sources were identified by the PMF model, including secondary inorganic aerosol (35.3%), coal combustion (28.7%), vehicular emission (20.7%), electroplating industry (8.6%), smelt industry (3.9%) and dust (2.8%). Locations of each identified source were pinpointed based on conditional probability function, potential source contribution function and concentration weighted trajectory, which showed that the geographical distribution of the sources of PM_2.5 roughly agreed with the areas of high emission. Overall, this study provides valuable information on atmospheric pollution and deems beneficial for policymakers to take informed action to sustainably improve air quality in highly polluted region.     

Investigation of PM2.5 pollution during COVID-19 pandemic in Guangzhou, China

The COVID-19 pandemic has raised awareness about various environmental issues,including PM_2.5 pollution.Here,PM_2.5 pollution during the COVID-19 lockdown was traced and analyzed to clarify the sources and factors influencing PM_2.5 in Guangzhou,with an emphasis on heavy pollution.The lockdown led to large reductions in industrial and traffic emissions,which significantly reduced PM_2.5 concentrations in Guangzhou.Interestingly,the trend of PM_2.5     

An integrated chemical mass balance and source emission inventory model for the source apportionment of PM2.5 in typical coastal areas

The source apportionment of PM_2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI) model to acquire more accurate results.First,the SEI of secondary component precursors(SO₂,NO_x,NH₃,and VOCs) was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contribut...     

Seasonal differences in sources and formation processes of PM2.5 nitrate in an urban environment of North China

Nitrate (NO₃⁻) has been the dominant ion of secondary inorganic aerosols (SIAs) in PM_2.5 in North China. Tracking the formation mechanisms and sources of particulate nitrate are vital to mitigate air pollution. In this study, PM_2.5 samples in winter (January 2020) and in summer (June 2020) were collected in Jiaozuo, China, and water-soluble ions and (δ¹⁵N, δ¹⁸O)-NO₃⁻ were analyzed. The results showed that the increase of NO₃⁻ concentrations was the most remarkable with increasing PM_2.5 pollution level. δ¹⁸O-NO₃⁻ values for winter samples (82.7‰ to 103.9‰) were close to calculated δ¹⁸O-HNO₃ (103‰ ± 0.8‰) values by N₂O₅ pathway, while δ¹⁸O-NO₃⁻ values (67.8‰ to 85.7‰) for summer samples were close to calculated δ¹⁸O-HNO₃ values (61‰ ± 0.8‰) by OH oxidation pathway, suggesting that PM_2.5 nitrate is largely from N₂O₅ pathway in winter, while is largely from OH pathway in summer. Averaged fractional contributions of P_N2O5+H2O were 70% and 39% in winter and summer sampling periods, respectively, those of P_OH were 30% and 61%, respectively. Higher δ¹⁵N-NO₃⁻ values for winter samples (3.0‰ to 14.4‰) than those for summer samples (-3.7‰ to 8.6‰) might be due to more contributions from coal combustion in winter. Coal combustion (31% ± 9%, 25% ± 9% in winter and summer, respectively) and biomass burning (30% ± 12%, 36% ± 12% in winter and summer, respectively) were the main sources using Bayesian mixing model. These results provided clear evidence of particulate nitrate formation and sources under different PM_2.5 levels, and aided in reducing atmospheric nitrate in urban environments.     

Characterization of peroxyacetyl nitrate (PAN) under different PM2.5 concentration in wintertime at a North China rural site

As a secondary pollutant of photochemical pollution, peroxyacetyl nitrate(PAN) has attracted a close attention. A four-month campaign was conducted at a rural site in North China Plain(NCP) including the measurement of PAN, O₃, NO_x, PM_2.5, oxygenated volatile organic compounds(OVOCs), photolysis rate constants of NO₂ and O₃ and meteorological parameters to investigate the wintertime characterization of photochemistry from November 2018 to Fe...     

Aerosol optical properties under different pollution levels in the Pearl River Delta (PRD) region of China

To clarify the aerosol hygroscopic growth and optical properties of the Pearl River Delta(PRD)region,integrated observations were conducted in Heshan City of Guangdong Province from October 19 to November 17,2014.The concentrations and chemical compositions of PM_2.5,aerosol optical properties and meteorological parameters were measured.The mean value of PM_2.5 increased from less than 35(excellent) to 35-75 μg/m³(good) and then to greater than 75 μg/m³(...     

Progressively narrow the gap of PM2.5 pollution characteristics at urban and suburban sites in a megacity of Sichuan Basin, China

Nowadays, the fine particle pollution is still severe in some megacities of China, especially in the Sichuan Basin, southwestern China. In order to understand the causes, sources, and impacts of fine particles, we collected PM_2.5 samples and analyzed their chemical composition in typical months from July 2018 to May 2019 at an urban and a suburban (background) site of Chengdu, a megacity in this region. The daily average concentrations of PM_2.5 ranged from 5.6-102.3 µg/m³ and 4.3-110.4 µg/m³ at each site. Secondary inorganics and organic matters were the major components in PM_2.5 at both sites. The proportion of nitrate in PM_2.5 has exceeded sulfate and become the primary inorganic component. SO₂ was easier to transform into sulfate in urban areas because of Mn-catalytic heterogeneous reactions. In contrast, NO₂ was easily converted in suburbs with high aerosol water content. Furthermore, organic carbon in urban was much greater than that in rural, other than elemental carbon. Element Cr and As were the key cancer risk drivers. The main sources of PM_2.5 in urban and suburban areas were all secondary aerosols (42.9%, 32.1%), combustion (16.0%, 25.2%) and vehicle emission (15.2%, 19.2%). From clean period to pollution period, the contributions from combustion and secondary aerosols increased markedly. In addition to tightening vehicle controls, urban areas need to restrict emissions from steel smelters, and suburbs need to minimize coal and biomass combustion in autumn and winter.     

Spatiotemporal optical properties of dissolved organic matter in a sluice-controlled coastal plain river with both salinity and trophic gradients

Due to the combined effect of sluices and sea tide, the sluice-controlled coastal plain river would be characterized by both trophic state and salinity gradients, affecting the spatiotemporal optical properties of dissolved organic matter (DOM). In this study, we investigated the spatiotemporal variation of water quality parameters and optical properties of DOM in the Haihe River, a representative sluice-controlled coastal plain river in Tianjin, China. A significant salinity gradient and four trophic states were observed in the water body of the Haihe River. Two humic- and one protein-like substances were identified from the DOM by the three-dimensional fluorescence spectra combined with the parallel factor (PARAFAC) analysis. Pearson's correlation analysis and redundancy analysis (RDA) showed that the salinity significantly affected the abundance of chromophoric DOM (CDOM) but did not cause significant changes in the fluorescence optical characteristics. In addition, the effect of Trophic state index (TSI) on the CDOM abundance was greater than that on the fluorescence intensity of fluorescent dissolved organic matter (FDOM). In the water body with both salinity and trophic state gradients, TSI posed a greater influence than salinity on the CDOM abundance. Our results fill the research gap in spatiotemporal DOM characteristics and water quality variation in water bodies with both salinity and trophic state gradients. These results are beneficial for clarifying the joint influence of saline intrusion and sluices on the DOM characteristics and water quality in sluice-controlled coastal plain rivers.     

Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China

The intraurban distribution of PM_2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM_2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM_2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R² compared with cross-validation results. For MLR models, inflation of both R² and R²_CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_2.5 levels. Inflated within-sample R² also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_2.5 concentrations.