Air Pollutant Concentration Data Research Articles

Unit Maxwell-Boltzmann Distribution and Its Application to Concentrations Pollutant Data

In the vast statistical literature, there are numerous probability distribution models that can model data from real-world phenomena. New probability models, nevertheless, are still required in order to represent data with various spread behaviors. It is a known fact that there is a great need for new models with limited support. In this study, a flexible probability model called the unit Maxwell-Boltzmann distribution, which can model data values in the unit interval, is derived by selecting the Maxwell-Boltzmann distribution as a base-line model. The important characteristics of the derived distribution in terms of statistics and mathematics are investigated in detail in this study. Furthermore, the inference problem for the mentioned distribution is addressed from the perspectives of maximum likelihood, method of moments, least squares, and maximum product space, and different estimators are obtained for the unknown parameter of the distribution. The derived distribution outperforms competitive models according to different fit tests and information criteria in the applications performed on four actual air pollutant concentration data sets, indicating that it is an effective model for modeling air pollutant concentration data.

Rapid weather changes are associated with daily hospital visitors for atrial fibrillation accompanied by abnormal ECG repolarization: a case-crossover study

BackgroundAtrial fibrillation (AF) is highly prevalent in the population, yet the factors contributing to AF events in susceptible individuals remain partially understood. The potential relationship between meteorological factors and AF, particularly with abnormal electrocardiograph (ECG) repolarization, has not been adequately studied. This case-crossover study aims to investigate the association between meteorological factors and daily hospital visits for AF with abnormal ECG repolarization in Shanghai, China.MethodsThe study cohort comprised 10,325 patients with ECG-confirmed AF who sought treatment at Shanghai Sixth People's Hospital between 2015 and 2018. Meteorological and air pollutant concentration data were matched with the patient records. Using a case-crossover design, we analyzed the association between meteorological factors and the daily count of hospital visitors for AF with abnormal ECG repolarization at our AF center. Lag analysis models were applied to examine the temporal relationship between meteorological factors and AF events.ResultsThe analysis revealed statistically significant associations between AF occurrence and specific meteorological factors. AF events were significantly associated with average atmospheric pressure (lag 0 day, OR 0.9901, 95% CI 0.9825–0.9977, P < 0.05), average temperature (lag 1 day, OR 0.9890, 95% CI 0.9789–0.9992, P < 0.05), daily pressure range (lag 7 days, OR 1.0195, 95% CI 1.0079–1.0312, P < 0.01), and daily temperature range (lag 5 days, OR 1.0208, 95% CI 1.0087–1.0331, P < 0.01). Moreover, a significant correlation was observed between daily pressure range and daily temperature range with AF patients, particularly those with abnormal ECG repolarization, as evident in the case-crossover analysis.ConclusionThis study highlights a significant correlation between meteorological factors and daily hospital visits for AF accompanied by abnormal ECG repolarization in Shanghai, China. In addition, AF patients with abnormal ECG repolarization were found to be more vulnerable to rapid daily changes in pressure and temperature compared to AF patients without such repolarization abnormalities.

Spatial and temporal characteristics analysis and prediction model of PM2.5 concentration based on SpatioTemporal-Informer model.

The primary cause of hazy weather is PM2.5, and forecasting PM2.5 concentrations can aid in managing and preventing hazy weather. This paper proposes a novel spatiotemporal prediction model called SpatioTemporal-Informer (ST-Informer) in response to the shortcomings of spatiotemporal prediction models commonly used in studies for long-input series prediction. The ST-Informer model implements parallel computation of long correlations and adds an independent spatiotemporal embedding layer to the original Informer model. The spatiotemporal embedding layer captures the complex dynamic spatiotemporal correlations among the input information. In addition, the ProbSpare Self-Attention mechanism in this model can focus on extracting important contextual information of spatiotemporal data. The ST-Informer model uses weather and air pollutant concentration data from numerous stations as its input data. The outcomes of the trials indicate that (1) The ST-Informer model can sharply capture the peaks and sudden changes in PM2.5 concentrations. (2) Compared to the current models, the ST-Informer model shows better prediction performance while maintaining high-efficiency prediction [Formula: see text]. (3) The ST-Informer model has universal applicability, and the model was applied to the concentration of other pollutants prediction with good results.

Association between ambient air pollutants and upper respiratory tract infection and pneumonia disease burden in Thailand from 2000 to 2022: a high frequency ecological analysis

BackgroundA pertinent risk factor of upper respiratory tract infections (URTIs) and pneumonia is the exposure to major ambient air pollutants, with short term exposures to different air pollutants being shown to exacerbate several respiratory conditions.MethodsHere, using disease surveillance data comprising of reported disease case counts at the province level, high frequency ambient air pollutant and climate data in Thailand, we delineated the association between ambient air pollution and URTI/Pneumonia burden in Thailand from 2000 – 2022. We developed mixed-data sampling methods and estimation strategies to account for the high frequency nature of ambient air pollutant concentration data. This was used to evaluate the effects past concentrations of fine particulate matter (PM2.5), sulphur dioxide (SO2), and carbon monoxide (CO) and the number of disease case count, after controlling for the confounding meteorological and disease factors.ResultsAcross provinces, we found that past increases in CO, SO2, and PM2.5 concentration were associated to changes in URTI and pneumonia case counts, but the direction of their association mixed. The contributive burden of past ambient air pollutants on contemporaneous disease burden was also found to be larger than meteorological factors, and comparable to that of disease related factors.ConclusionsBy developing a novel statistical methodology, we prevented subjective variable selection and discretization bias to detect associations, and provided a robust estimate on the effect of ambient air pollutants on URTI and pneumonia burden over a large spatial scale.

A spatial correlation prediction model of urban [formula omitted] concentration based on deconvolution and LSTM

Precise prediction of air pollutants can effectively reducre the occurrence of heavy pollution incidents. With the current surge of massive data, deep learning appears to be a promising technique to achieve dynamic prediction of air pollutant concentration from both the spatial and temporal dimensions. This paper presents Dev-LSTM, a prediction model building on deconvolution and LSTM. The novelty of Dev-LSTM lies in its capability to fully extract the spatial feature correlation of air pollutant concentration data, preventing the excessive loss of information caused by traditional convolution. At the same time, the feature associations in the time dimension are mined to produce accurate prediction results. Experimental results show that Dev-LSTM outperforms traditional prediction models on a variety of indicators.

Analysis of urban air quality and pollutant changes in Shandong Province from 2015 to 2019

In this paper, the heavily polluted cities of Jinan, Zibo, Dongying and Linyi in Shandong Province were taken as the study area. Based on the daily air quality and pollutant concentration data from 2015 to 2019, the change of air quality and pollutant concentration in the study area was analyzed from four time scales of year, month, working day and day by time series analysis, combined with meteorological, industrial and other influencing factors. The analysis showed that: (1) With the implementation of the blue Sky Defense plan in 2018, the air quality in the study area was significantly improved, and the decrease of SO2, PM2.5 and CO was the largest, the percentage reduction was 41.5%, 62.36%, 39.35%; (2): Monthly variation: it shows a "W" -shaped variation, with severe pollution in winter and light pollution in early autumn; (3) Working days and rest days show an "S" -shaped change, with Tuesday as the pollution trough and Saturday as the pollution peak; (4) A "convex" type of fluctuation was observed, with the heaviest pollution in the middle of the month and lighter pollution in the beginning and end of the month.

The Characteristics of PM2.5 and PM10 and Elemental Carbon Air Pollution in Sevastopol, Crimean Peninsula

In most cities of the world, air pollution reaches critical levels. The air masses circulating over the Crimean Peninsula bring a significant amount of mineral dust, which contains soil particles, emissions from industrial enterprises, gases, etc. The purpose of this research is to study the processes and the factors influencing atmospheric pollution in Sevastopol (Crimea). Air pollutant concentration data, including elemental carbon, nutrients (inorganic fixed nitrogen, inorganic fixed phosphorus and silicon), PM10, and PM2.5, were collected during this research. Samples were collected at the station that is located at a distance from sources of pollution (background station). Our study has shown that even at the background site the daily-averaged concentrations of PM10 and PM2.5 particles in the atmosphere of Sevastopol reach and even exceed the maximum permissible concentrations in the case of dust transported from deserts. Values of the daily-averaged concentrations of microparticles have exceeded the European maximum permissible concentration (MPC) values in 17 cases for PM2.5 particles and in 6 cases for PM10. The impact of both local sources and long-distance atmospheric transport depends on weather conditions. Concentrations of elemental carbon in air samples have never exceeded the maximum allowed by regulations concentration limits during our research. However, the elemental carbon concentration in air samples collected near highways with a traffic intensity of approximately 500–1000 cars per hour has exceeded the background values by 30–50 times.

Air Pollution Monitoring in Remote Areas using Smart UAV based System

Real time monitoring of air pollution and measurement of pollutant gases is very challenging task and needs more numbers of sensors and lots of observation. In this paper, Unmanned Aerial Vehicle (UAV) based system equipped with different micro sensor, is introduced for monitoring of air quality. It will offer a new approach in environmental pollution assessment instead of ground based monitoring system. Air pollution concentration data is collected by different sensors present in UAV, are effectively monitored in personal computer or mobile devices. The main objective of this paper is to elaborate the performance capability of UAV for effective monitoring of air pollution and measure health hazard air pollutants with high sensitivities in a particular area where human cannot reach. As it is quite recent field, a fruitful effort has dedicated to develop an integrated sensing system and optimization of its crucial features as dimension, weight and energy autonomy. The effectiveness of the developed system is evaluated by performing some field experiments using a hardware prototype UAV model.

A GIS-Based Study of Air and Water Quality Trends in Madurai City, India

The objective of this study is to assess the air quality and water quality at Madurai city using the spatial interpolation technique of the Geographic Information System (GIS). Inverse Distance Weighting (IDW), Kriging, Gaussian- Kriging methods of interpolation are used in the study. The study area is divided into 8 zones and primary data is collected through direct sampling for water. The air pollution concentration data on Suspended Particulate Matter (SPM), Respirable Particulate Matter (RPM), and Lead are collected from an air quality monitoring network system of Tamilnadu Pollution Control Board (TNPCB). The results indicate that the Kriging interpolation method performs well with an error value of 0.02. The data collected from the years 2006 and 2020 are compared using GIS and observed data. The comparison of results indicates that there is a good accord with the predicted value using Inverse Distance Weighting (IDW) and Kriging interpolation with the observed data. The analysis report indicates that the pollution level at all 8 blocks was severely affected in 2020 and it is above the prescribed standard of TNPCB. Particularly, Kalavasal, Arapalyam, and Periyar blocks are severely affected. This work also presents suitable mitigation measures to reduce pollution.

Multi‐step‐ahead prediction interval for locally stationary time series with application to air pollutant concentration data

Locally stationary time series frequently appears in both finance and environmental sciences (e.g., daily air pollutant concentration or financial returns). However, constructing the multi‐step‐ahead prediction interval for such time series remains an open question. Hence, we extend the nonparametric regression model with autoregressive errors for equally spaced designs to the time series setup. We propose a B‐spline estimator for the trend function and a kernel estimator for the variance function to implement the model. The prediction interval of multi‐step‐ahead future observations is also constructed after fitting the autoregressive model of errors and obtaining the quantile of prediction residuals. The proposed method is illustrated by various simulation studies and an example of air pollutant data, containing 8 years of daily air pollutant concentrations in Xi'an. Our results demonstrate that our method outperforms others owing to its higher prediction accuracy and versatility.

Seasonal Patterns and Trends of Air Pollution in the Upper Northern Thailand from 2004 to 2018

The seasonal patterns and trends of air pollutants can be used in assessments of air quality, facilitating authorities’ decisions on policies for monitoring and managing. The objective of this study was to investigate the seasonal patterns and trends of air pollutants in the upper northern Thailand (UNT) from 2004 to 2018. The hourly air pollutant concentration data including carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NOx), sulfur dioxide (SO2), ozone (O3), and particulate matter ≤ 10 µm (PM10) recorded from 6 monitoring stations in the UNT were obtained from the Pollution Control Department, Ministry of Natural Resources and Environment of Thailand. Cubic splines were used to assess seasonal patterns and trends of the air pollutants. Linear regression was used to estimate the average increase in concentrations of air pollutants at each monitoring station. The results exhibited seasonal patterns for CO, NOx, NO2, O3 and PM10, in all stations while SO2 exhibited seasonal patterns only in one station in Lampang and all stations in Chiangmai. The concentrations of these pollutants rose during August and September and reached peak levels in March. In the past 15 years, the levels of overall CO, O3 and SO2 in the UNT had significantly increased, on average by 0.015 ppm, 0.012 ppb and 0.015 ppb, respectively. In contrast, NO2, NOx and PM10 had significantly decreased on average of –0.010 ppb, –0.008 ppb and –0.011 µg m–3, respectively. The results provide information to the authorities for setting up proper measures and policies to control and mitigate air quality impacts especially in the dry season.

Evaluation of multivariate time series clustering for imputation of air pollution data

Abstract. Air pollution is one of the world's leading risk factors for death, with 6.5 million deaths per year worldwide attributed to air-pollution-related diseases. Understanding the behaviour of certain pollutants through air quality assessment can produce improvements in air quality management that will translate to health and economic benefits. However, problems with missing data and uncertainty hinder that assessment. We are motivated by the need to enhance the air pollution data available. We focus on the problem of missing air pollutant concentration data either because a limited set of pollutants is measured at a monitoring site or because an instrument is not operating, so a particular pollutant is not measured for a period of time. In our previous work, we have proposed models which can impute a whole missing time series to enhance air quality monitoring. Some of these models are based on a multivariate time series (MVTS) clustering method. Here, we apply our method to real data and show how different graphical and statistical model evaluation functions enable us to select the imputation model that produces the most plausible imputations. We then compare the Daily Air Quality Index (DAQI) values obtained after imputation with observed values incorporating missing data. Our results show that using an ensemble model that aggregates the spatial similarity obtained by the geographical correlation between monitoring stations and the fused temporal similarity between pollutant concentrations produces very good imputation results. Furthermore, the analysis enhances understanding of the different pollutant behaviours and of the characteristics of different stations according to their environmental type.

Integration of air quality model with GIS for the monitoring of PM2.5 from local primary emission at a rural site.

Local primary emissions of air pollutants are responsible for public health, decreasing productivity, and cultural activities in local residential areas. In this study, an integrated air quality observation and modeling system with a geographical information system (GIS) was developed to characterize the air pollution caused by local primary emission sources. This integrated system could provide air quality monitoring, data analysis, and visualization results that reflect air pollutant concentration data in a study area containing a local rural village (LRV) and an asphalt manufacturing facility (AMF). Additionally, the model was used to estimate the contributions of air quality from an emission source at the receptor and determine the control factor for the emission rate or meteorological changes. From the forward and backward modeling results, we found that the concentrations of particulate matter smaller than 2.5μm (PM2.5) concentrations in the village were affected by the unique meteorological and emission conditions. The PM2.5 concentration was significantly increased for the cases with a slow wind speed of 1m/s or high wind speed of 3m/s, with an emission rate of 10g/s. The contribution of AMF emissions was explained by contribution factor analysis. During the study period of December 2014-December 2015, the incoming contribution of PM2.5 at the LRV measurement station was approximately 47.6%. These results suggest that the proposed method can be useful for understanding adverse air quality conditions and estimating the emissions of air pollutants from primary sources for local environmental and public health authorities.

The sensitivity of simulated surface-level pollution concentrations to WRF-ARW-model PBL parameterisation schemes over the Highveld of South Africa

An understanding of the sensitivity of pollutant concentrations to planetary boundary layer (PBL) schemes in numerical weather prediction (NWP) models can provide insight into model results, and possibly advance the accuracy of air pollution modelling. Meteorological output from the Advanced Research Weather Research and Forecasting (WRF-ARW) model, with four frequently used PBL schemes, is used as input for an air quality model. The simulations resulting from the different schemes are compared with each other and evaluated making use of observational air pollution concentration data for a spring and a winter month of 2016 in South Africa. Sensitivity exists in surface-level pollution concentrations as simulated by the considered PBL schemes. No single PBL scheme consistently performed best across the five considered pollutants, five sites and two seasons. During analysis of the air quality simulations, it was found that the two local closure schemes – Mellor-Yamada-Janjić (MYJ) and Mellor-Yamada Nakanishi Niino (MYNN) – often performed well, while the non-local closure schemes – Yonsei University (YSU) and Asymmetric Convection Modelling (ACM) – featured as best-performing schemes the least number of times. Sensitivity also exists on a spatial scale between schemes when simulating sulphur dioxide (SO2) plumes from tall stacks, and the MYNN scheme frequently produced the highest concentrations of SO2. The results from this study should be considered when configuring NWP models for the simulation of air quality in the Highveld region.

Design and implementation of air pollution management system and an application case in Beijing

In recent years, the air pollution in China is becoming more and more serious, especially in Beijing. In this paper, an air pollution information management system was established by using ComGIS component of ArcEngine and Visual C# .NET platform and SQL Server technology. The system consists of three modules, including basic function, spatial analysis and early warning. It can monitor air pollution from different angles. In order to realize the simulation of pollution diffuse and the visual management of air pollution, we developed this system to monitor and analyse the air pollution situation in Beijing in quasi real time. Air pollution concentration data recorded by the monitoring station in Beijing on May 23, 2015 was obtained. The results showed that the severe atmospheric pollution in Beijing was mainly distributed in Chaoyang, Xicheng, Dongcheng and Fengtai districts. The area with severe PM2.5 exceeding the standard was located near the Monitoring station of Yizhuang Development. However, it indicated that the severely polluted areas extended from Daxing and Tongzhou districts to Xicheng, Dongcheng and Chaoyang districts when simulating the diffusion of PM2.5 pollutants by adding wind power and wind direction data on the same day.

Nitrogen dioxide exposure during pregnancy and risk of spontaneous abortion: a case-control study in China

Background Evidence on the relationship between prenatal exposure to NO2 and CO and spontaneous abortion (SAB) is insufficient. We investigated whether there is an association between maternal exposure to nitrogen dioxide (NO2) and carbon monoxide (CO) before and during pregnancy and SAB. Methods We conducted a case-control study using medical records of 2445 pregnant women who admitted for abortion prior to 20 weeks of gestational age from January 2014 to December 2019 at a tertiary-care hospital in Shanghai, China. Of the 2445 participants, 1075 were SAB cases and 1370 were healthy controls (underwent elective abortions). Maternal exposure to NO2 and CO before and during pregnancy was estimated using daily air pollution concentration data. Multivariable logistic regression models were constructed to quantify the relationships between maternal exposure to NO2 or CO and the risk of SAB while controlling for potential confounders. Results NO2 exposure levels during pregnancy were significantly higher in SAB cases than in healthy controls (42.26 vs. 40.67, p < .01). NO2 exposure during pregnancy was positively associated with the risk of SAB. An interquartile range (16 μg/m3) increase in NO2 exposure was associated with 68% increase in the odds of SAB (OR = 1.68, 95% CI, 1.28, 2.21). Analyses of associations by quartile of NO2 exposure showed that elevated NO2 exposure during pregnancy was associated with increased odds of SAB in linear dose–response manners. Compared with the lowest quartile of NO2 exposure, the odds of SAB in the fourth quartile of NO2 exposure increased 61% (OR = 1.61, 95% CI, 1.03–2.53). No associations of CO exposure with SAB risk were observed. Conclusions Our study suggested that exposure to NO2 during early pregnancy was associated with increased risk of SAB. Further studies are needed to confirm our results and explore the potential biological mechanism underlying these associations.

Air-pollutant mass concentration changes during COVID-19 pandemic in Shanghai, China

To curb the spread of the coronavirus, China implemented lockdown policies on January 23, 2020. The resulting extreme changes in human behavior may have influenced the air pollutants concentration. However, despite these changes, hazy weather persisted in Shanghai and became a public issue. This study aims to investigate air pollutant mass concentration changes during the lockdown in Shanghai. Air pollutant mass concentration data and meteorological data during the pre-lockdown period and the level I response lockdown period were analyzed by statistical analysis and a Lagrangian particle diffusion model. The data was classified in three periods: P1 (pre-lockdown: 10 days before the Spring Festival), P2 (the first 10 days after lockdown: during the Spring Festival celebration), and P3 (the second 10 days after lockdown: after the Spring Festival). Data for the same period in 2019 were used as a reference. The results indicate that the Spring Festival holiday in 2019 resulted in a reduction in energy consumption, which led to a decrease in PM2.5 (26.4%) and NO2 (43.41%) mass concentration, but an increase in ozone mass concentration (31.39%) in P2 compared with P1. The integrated effect of the Spring Festival holiday and lockdown in 2020 resulted in a decrease in PM2.5 (36.5%) and NO2 (51.9%) mass concentrations, but an increase in ozone mass concentration (43.8%) in P2 compared with P1. After the Spring Festival, the mass concentrations of PM2.5, SO2, and NO2 increased by 74.41%, 5.52%, and 53.28%, respectively in P3 compared with P2 in 2019. However, PM2.5 and SO2 concentrations in 2020 continued to decrease, by 14.74% and 4.61%, respectively, while NO2 mass concentration increased by 7.82% in P3 compared with P2. We also found that PM2.5 mass concentration is susceptible to regional transmission from the surrounding cities. PM2.5 and other gaseous pollutants show different correlations in different periods, while NO2 and O3 always show a strong negative correlation. The principal components before the Spring Festival in 2019 were O3 and NO2, and after the Spring Festival, they were PM2.5 and CO, while the principal components before the lockdown in 2020 were PM2.5 and CO, and during lockdown they were O3 and NO2.Electronic supplementary materialThe online version of this article (10.1007/s11869-020-00956-x) contains supplementary material, which is available to authorized users.

Air Pollutant Concentration Forecasting Using Long Short-Term Memory Based on Wavelet Transform and Information Gain: A Case Study of Beijing.

Air pollutant concentration forecasting is an effective way which protects health of the public by the warning of the harmful air contaminants. In this study, a hybrid prediction model has been established by using information gain, wavelet decomposition transform technique, and LSTM neural network, and applied to the daily concentration prediction of atmospheric pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Beijing. First, the collected raw data are selected by feature selection by information gain, and a set of factors having a strong correlation with the prediction is obtained. Then, the historical time series of the daily air pollutant concentration is decomposed into different frequencies by using a wavelet decomposition transform and recombined into a high-dimensional training data set. Finally, the LSTM prediction model is trained with high-dimensional data sets, and the parameters are adjusted by repeated tests to obtain the optimal prediction model. The data used in this study were derived from six air pollution concentration data in Beijing from 1/1/2014 to 31/12/2016, and the atmospheric pollutant concentration data of Beijing between 1/1/2017 and 31/12/2017 were used to test the predictive ability of the data set test model. The results show that the evaluation index MAPE of the model prediction is 7.45%. Therefore, the hybrid prediction model has a higher value of application for atmospheric pollutant concentration prediction, because this model has higher prediction accuracy and stability for future air pollutant concentration prediction.

A study on extending the use of air quality monitor data via deep learning techniques

As the environmental awareness of urban citizens increases, traditional air quality monitoring stations cannot satisfy the need for air quality data at high temporal and spatial resolution due to their high construction and maintenance costs. Low-cost air quality monitors are being increasingly used for this purpose because of their portability and affordable price. However, low-cost monitors are usually beset by data quality issues, and the number of mounted air pollutant sensors is limited by the restriction of the cost and size of monitors. Therefore, we propose to extend the use of air quality monitor data via a deep learning technique called long short-term memory (LSTM). The extension is embodied in two aspects: first, calibration of air pollutant concentration data; and second, provision of indicative information about air pollutants where no corresponding sensors are available. A low-cost air quality monitor called KOALA (Knowing Our Ambient Local Air Quality), which was deployed in Sydney (Australia), was used as an example to prove this method’s feasibility. Data from a 90-day period were used for model training, and data from a 30-day period were used for model validation. Random forest models were used for selecting the most useful LSTM model input variables. Historical 24-h information was incorporated to improve the performance of the LSTM models. The results showed that: first, LSTM models can be used to calibrate KOALA carbon monoxide (CO) data with the optimum input being raw CO measurements and the corresponding standard deviation information; and second, LSTM models can be used to estimate ozone concentration with the optimum input being CO concentration and three meteorological parameters [i.e. top soil layer temperature, 10 m U wind (earth-relative), and net shortwave radiation flux at the ground] generated through a deterministic model known as WRF (Weather Research and Forecasting). In addition, the LSTM ozone estimation model showed good performance at both the training location and a location 11 km away, indicating that the proposed method can be used to provide indicative information about air pollutants around the training location.

Chemical characteristics and source apportionment of PM2.5 using PMF modelling coupled with 1-hr resolution online air pollutant dataset for Linfen, China

The chemical species in PM2.5 and air pollutant concentration data with 1-hr resolution were monitored synchronously between 15 November 2018 and 20 January 2019 in Linfen, China, which were analysed for multiple temporal patterns, and PM2.5 source apportionment using positive matrix factorisation (PMF) modelling coupled with online chemical species data was conducted to obtain the apportionment results of distinct temporal patterns. The mean concentration of PM2.5 was 124 μg/m3 during the heating period, and NO3− and organic carbon (OC) were the dominant species. The concentrations and percentages of NO3−, SO42−, and OC increased notably during the growth periods of haze events, thereby indicating secondary particle formation. Six factors were identified by the PMF model during the heating period, including vehicular emissions (VE: 26.5%), secondary nitrate (SN: 16.5%), coal combustion and industrial emissions (CC&IE: 25.7%), secondary sulfate and secondary organic carbon (SS&SOC: 24.4%), biomass burning (BB: 1.0%), and crustal dust (CD: 5.9%). The primary sources of PM2.5 on clean days were CD (33.3%), VE (23.1%), and SS&SOC (20.6%), while they were CC&IE (32.9%) and SS&SOC (28.3%) during the haze events. The contributions of secondary sources and CC&IE increased rapidly during the growth periods of haze events, while that of CD increased during the dissipation period. Diurnal variations in the contribution of secondary sources were mainly related to the accumulation and transformation of corresponding gaseous precursors. In comparison, contributions of CC&IE and VE varied as a function of the domestic heating load and peak levels occurred during the morning and evening rush hours. High contributions of major sources (CC&IE and SS&SOC) during haze events originated mainly from the north and south, while high contribution of a major source (CD) on clean days was from the northwest.