Online Monitoring Data Research Articles

Runoff Estimation of Jiulong River Based on Acoustic Doppler Current Profiler Online Monitoring Data and Its Implication for Pollutant Flux Estimation

The runoff of the Jiulong River (JLR) is a key parameter that affects the estimation of pollutant flux into Xiamen Bay (XMB). The precise runoff estimation of the JLR can be used to determine the accuracy of the pollutant flux estimation flowing into XMB. In this study, to analyze the hydrological dynamic characteristics and identify the correlation between fixed-site real-time ocean current observations and cross-sectional navigation flow observations, we conducted six navigation observations on two cross-sections of the JLR estuary during the spring tide and neap tide in the normal season, wet season, and dry season in 2020. Simultaneously, we measured hydrological observation data by a fixed-site buoy located in the JLR estuary and collected runoff data that were measured upstream of the JLR. The results showed that the average correlation coefficient between the average velocity of the fixed-point buoy and average velocity of the section was more than 0.90, higher than expected, the minimum average deviation was 4%, and the minimum sample standard error was 5.7%, which was a good result. In this study, we constructed a model for estimating the runoff of the JLR into the sea. The findings demonstrated that Acoustic Doppler Current Profiler (ADCP) online monitoring data were useful to estimate runoff of the JLR with high accuracy, could promote the accuracy of estimated pollutant flux of the JLR's discharge into XMB, and could provide more scientific and reliable basic data for future load flux estimation research.

Sensitivity of Ozone Formation in Summer in Jinan Using Observation-Based Model

According to online monitoring data on atmospheric ozone and the pollution characteristics of its precursors obtained in Jinan in June 2021, we analyzed different sites: urban sites (city monitoring station, Quancheng Square), an industrial park site (oil refinery), and a suburban site (Paomaling). The relative incremental reactivity of different precursors was calculated using a photochemical observation-based model to explore the sensitivity of O3 generation at each site and to draw a curve using the empirical kinetics modeling approach. The PMF model was used to analyze the origin of volatile organic compounds (VOCs) pollution in Jinan. The results showed that the concentration of O3 at the industrial park was higher than that in the urban area in Jinan, which may be related to the fact that ozone precursor concentrations in the industrial park were significantly higher than those in the urban area (the AVOCs concentration at the industrial park site was 56.9 ppbv, approximately twice that of the urban site), and there are emission peaks at night; alkanes, oxygenated compounds, and halogenated hydrocarbons were the main components of the AVOCs, and olefins, alkanes, and aromatic hydrocarbons were the main active components in Jinan. The O3 generation in urban areas generally occurred in the VOCs-sensitive zones, while the O3 generation in the other areas occurred in the VOCs-NOx transition zone; there was a clear diurnal variation in the sensitivity of the industrial park, with the site being in the obvious VOCs-sensitive zone from nighttime to morning hours and shifting to the VOCs-NOx transition zone in the afternoon hours; the relative incremental reactivity (RIR) value of AVOCs for O3 generation in Jinan was the largest, and olefins were the most sensitive component of O3. The AVOCs in Jinan mainly originated from motor vehicle exhaust, oil and gas volatilization, industrial emissions, and solvent use, and ozone prevention and control in summer should strengthen the control of these sources.

Study on structural mechanical characteristics and safety warning of NATM tunnel in aquifer

With sustainable developments in transportation facilities, the construction of the undersea tunnel has become inevitable. Controlling the construction and operation of the undersea tunnel structures dynamically and ensuring their safety is of primary concern. Therefore, using the rich water mountain section of the Lujiazhi subsea tunnel constructed by NATM (New Austrian Tunnelling Method) as a case study, an intelligent real-time tunnel health monitoring system integrating tunnel monitoring, wireless transmission and safety assessment was developed. The monitoring results show that the surrounding rock water pressure, earth pressure and stress of the secondary lining structure experienced stages of rapid change, continuous change and stability. The tunnel substructure is relatively weak as a whole, and the left invert is the weakest part of the whole structure. It was suggested to optimize and reinforce the tunnel substructure accordingly. The rationality of the tunnel drainage system and reliability of field test data are verified by numerical simulation. Finally, the real-time fuzzy comprehensive evaluation model of tunnel operation safety conditions based on online monitoring data is established to realise the real-time evaluation and early warning of Lujiazhi tunnel structures’ health conditions. HIGHLIGHTS An intelligent real-time tunnel health monitoring system is developed. The deformation characteristics of submarine tunnel are revealed. The submarine tunnel drainage system rationality of lining structure are proved. The safety evaluation model of submarine tunnel structure is proposed.

Water quality prediction of MBR based on machine learning: A novel dataset contribution analysis method

With the advent of the big data era, data-driven analysis to realize the mining of internal laws of data has gradually become a developmental trend in sewage management and decision-making. This paper explored the regression algorithm in machine learning (ML) including linear model, decision tree, ensemble learning, nearest neighbor model, support vector machine to predict the water quality of membrane bioreactor (MBR) in treating high salt ammonia nitrogen wastewater. Using conventional online monitoring data as characteristic variables, the concentrations of NH4+-Nout, NO3−-Nout, NO2−-Nout, CODout, and TNout were targeted for prediction. The parameter-adjusted learning curve and grid-search strategy optimized each training model. The importance of each input feature on the prediction target was analyzed. Finally, to better use wastewater enterprises' long-term accumulated water quality data and reduce the management difficulty and operation cost, this paper proposed a dataset contribution degree (DCD) analysis method by combining ML and evaluation indicators. The results showed that the integration algorithms had better performance in the training process and the accuracy of prediction results. Combining the datasets improved the model's prediction performance, and the raw data accumulation under the impact of salinity enhanced saline wastewater predictability.

Unmanned stationary online monitoring system based on buoy for marine gamma radioactivity

Research on unmanned online monitoring equipment for marine radioactivity surrounding nuclear power plants is of great significance. In this work, a small radioactivity monitoring system based on buoy was designed and manufactured for the emergency situation of nuclear accidents. The core of the radioactivity monitoring system is the underwater gamma spectrometer. The spectrometer can respond to gamma rays from 60 keV to 3 MeV, and can identify the nuclides whose characteristic rays belong to this energy range. The detection efficiency curve was calculated through Monte Carlo simulation and verified in a standard liquid source. A data acquisition processor was also designed to coordinate the detectors in the system and wirelessly transmit online monitoring data. Three experiments were carried out in the seawater around the Tianwan Nuclear Power Plant in Lianyungang, China using this online marine radioactivity monitoring system based on buoys. The stability and radioactivity monitoring capabilities of the system have been verified.

Development of an End State Vision to Implement Digital Monitoring in Nuclear Plants

Nuclear plant sites collect and store large volumes of data gathered from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning (ML) and artificial intelligence (AI) technologies to provide diagnostic and prognostic capabilities within the nuclear power industry, thus reducing operations and maintenance (O&M) costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature plant closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging ML and AI technologies to develop data-driven algorithms that better diagnose and predict potential faults within the system. Improved model accuracy can help reduce unnecessary maintenance and foster more efficient planning of future maintenance, thereby lowering the costs associated with parts, labor, and costly planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from routine-based monitoring to online monitoring by taking advantage of advancements in sensors and wireless communication technologies. Advancements in data storage, mapping, management, and analytics would inform the transition from onsite- to cloud-based computing and storage services. Online monitoring would reduce the number of operator manhours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained all while scaling to both computational and storage demands. This paper summarizes an end state vision of how to shift from costly, labor-intensive preventative maintenance to cost-effective predictive maintenance.

Research on the on-line fracture assessment of cracked primary loop pipes in NPPs

The cracked pipes usually need to be replaced or conservatively evaluated in the Nuclear Power Plants (NPPs). There is still a lack of effective methods for a real-time crack propagation and safety evaluation based on massive monitoring transient data. In order to ensure the safe and economic operation of the NPPs, the authors have developed an on-line fracture assessment technology in this paper. The calculation method of total stress suitable for on-line monitoring data, optimizing the calculation method of transient thermal stress and stress intensity factor (SIF) based on the influence function method are discussed. Then, the on-line software for the primary loop hot-leg pipe which is based on the research results is developed. In this paper, an on-line safety assessment method based on the monitoring data which can meet the requirements of the in-service inspection code (RSE-M Code) is given. The influence function is proposed to be established at a fixed time interval which will make the computational complexity reduced significantly. By the parallel calculation method and the characteristics of the algorithm, the origin algorithm along the time axis is optimized to an algorithm that can be calculated to obtain the target parameters at each time point within the specified attenuation time range of the influence function. Then the calculation efficiency is significantly improved. By compiling the data and calculation methods in the code, the calculation method of SIF suitable for on-line monitoring is obtained, and the reliability is also verified by the analyzed case. Based on the technical results obtained from the research and the requirements in the RSE-M Code, the re-development is carried out in the general commercial software platform. The software is developed, and it is demonstrated that it has sufficient analysis accuracy.

A Data-Driven Self-Learning Evaluation Method of Voltage Sag Severity

Lightning is one of the main causes of voltage sags, and it is of great significance for subsequent analysis and governance to accurately evaluate the severity of the sag events caused by lightning. There are many uncertain factors between lightning fault events and voltage sag events. To evaluate the severity of voltage sag events caused by lightning, a data-driven self-learning evaluation method for voltage sag severity is proposed. According to a large number of online monitoring data of lightning positioning system and power quality monitoring system, the association rule mining algorithm based on incremental learning is used. And the rules are kept updating through the accumulation of historical data, which may give it the abilities of self-learning. The empirical analysis is carried out based on the monitoring data of a regional power grid. The results show that the method in this paper can accurately mine more valuable rules in reality and solve the problem of low efficiency of mining algorithm when the database changes dynamically.

On-line Monitoring Data Analysis and Its Application in Marine Environmental Monitoring

On-line Monitoring Data Analysis and Its Application in Marine Environmental Monitoring

Online state-of-health estimation of lithium-ion battery based on relevance vector machine with dynamic integration

The lithium-ion battery’s state of health (SOH) is one of the essential parameters of the battery management system. An accurate state of health estimation of the battery pack helps to improve the service life of the overall battery pack. Given the poor generalization ability of a single data-driven model during SOH online estimation and the lack of uncertainty expression ability in the estimation results, this paper proposes an online SOH estimation method of lithium-ion battery based on bat algorithm optimized relevance vector machine (BA-RVM) with dynamic integration. Firstly, we perform feature extraction and select equal voltage drop discharge time as an indirect health factor. Secondly, we establish the integration model, take the wavelet kernel relevance vector machine (RVM) as the sub-model, and use the bat algorithm (BA) to optimize its kernel parameters to improve the estimation accuracy of the sub-model. Then we use the online monitoring data to update the weights of the sub-models continuously and dynamically integrate the output of the sub-models to improve the accuracy of SOH online estimation further. Finally, the correctness and effectiveness of the method are verified based on battery data from NASA and compared with other data-driven methods. The experimental results show that compared with the method based on a single data-driven model, this method has higher accuracy and more vital generalization ability, and the estimation results have specific uncertainty expression ability.

Application of Big Data Technology in Environmental Pollution Control in Energy Ecological Economic Zone

In order to solve the problem of environmental pollution control in the energy ecological economic zone, this paper proposes a technology to realize environmental pollution control by using big data. The main content of this technology is based on the characteristics and application of big data technology; through the collection of online monitoring data of ecological environment pollution, the construction of the online monitoring data quality model of ecological environment pollution, and finally through the experiments in the simulation environment, the feasibility of big data technology is obtained. The experimental results show that the average quality factor data of the quality control model in the environmental scientific data supervision is 2.16, the average quality factor data of the Internet of things in the marine pollution monitoring is 5.88, and the average quality factor data of the model in the ecological environment pollution online monitoring based on the big data technology is 8.64. Big data technology has good applicability. It is proven that the research of big data technology can meet the needs of environmental pollution control in the energy ecological economic zone.

Online monitoring and sampling analysis of siltation in the middle route of the south-to-north water diversion project

The middle route project (MRP) of the South to North Water Diversion Project is a significant infrastructure and alleviating water scarcity in Northern China. MRP suffered from untraditional siltation problems. Obvious siltation occurred in the regulating reservoir at the end of the channel and some locations with weak hydrodynamic conditions in the channel when the mineral siltation concentration in the flow is very low. To study the characteristics of the siltation and the siltation time period, an IoT based automatic siltation monitoring system using cloud was installed at the outlet of the inverted siphon project on Xiao River. Three years of online monitoring data since 2018 and the siltation samples at five sites for particle size analysis were collected. The monitoring data shows that siltation mainly occurs during March to October, and almost no siltation occurs in winter. The maximum siltation speed can reach 390 mm per day. The particle size of the siltation gradually increases from upstream to downstream, which mainly occurs in the range above 100 m. The organic matter contained in the siltation shows a significant increase from 40.3 to 86.4% at upstream and downstream sampling position, respectively. Monitoring results shows the main body of the siltation in the MRP is not the traditional siltation but the remnants of the algae that proliferate in large numbers. During March to October, the temperature is suitable for the proliferation of algae which attaches to the sediment particles and gradually grows downstream with the flow.

Characteristics and Meteorological Factors of PM2.5-O3 Compound Pollution in Tianjin

The characteristics, pollutant concentration distribution, and key meteorological factors of PM2.5-O3 compound pollution in Tianjin were analyzed based on the high-resolution online monitoring data of PM2.5, O3,and meteorological data observed in Tianjin from 2013 to 2019. Total PM2.5-O3 compound pollution was 94 days and showed a decreasing trend by year; a significant decreasing trend of PM2.5-O3 compound pollution days were observed in the early stage, with a decline rate of 52.2% from 2013 to 2015. By contrast, in the later period from 2016 to 2019, a fluctuating increasing trend of PM2.5-O3 compound pollution days of 16.7% was observed. PM2.5-O3 compound pollution days mainly occurred from March to September each year with substantial variation by year, mainly occurring in June to August from 2013 to 2016 and in April and September from 2017 to 2019. The peak value of ρ(O3) (301-326 μg·m-3) appeared when ρ(PM2.5) ranged from 75 μg·m-3 to 85 μg·m-3. PM2.5-O3 compound pollution days accounted for 34.4% of total O3 pollution events in Tianjin, which showed a decreasing trend by year. The peak O3 concentration and average O3 concentration during PM2.5-O3 compound pollution were higher than those during simplex O3 pollution, and the number of days with PM2.5 and O3 as the primary pollutant decreased and increased in compound pollution days by year, respectively. The weather situation of PM2.5-O3 compound pollution was categorized into five weather types, namely low pressure, weak high pressure, rear of high pressure, front of cold front, and equalized pressure. The low pressure, front of cold front, and weak high pressure were observed most frequently, accounting for 92.5% of the total weather situation. The occurrence of PM2.5-O3 compound pollution was most probable when the dominant wind direction was the southwest and south, the average wind speed was less than 2 m·s-1, the temperature was between 20-35℃, and the humidity was between 40%-60%.

Pollution Characteristics and Source Apportionment of Atmospheric Volatile Organic Compounds in Summer in Yuncheng City

Based on the online monitoring data of VOCs, O3, and NO2 in Yuncheng City from June to August 2020, the pollution characteristics of VOCs in Yuncheng City in summer were analyzed. At the same time, the main emission sources were determined using a PMF model, and the chemical reactivity of VOCs was evaluated using the maximum incremental reactivity (MIR) method and fractional aerosol coefficients (FAC). The results showed that the urban area of Yuncheng was seriously polluted by VOCs and NO2 in the early morning and evening during summer, the peak value of VOCs daily variation occurred at 08:00 and 20:00, respectively, and was mainly affected by the morning and evening peaks in traffic. The ρ(VOCs) from June to August was 50.52 μg·m-3, and the species with the highest proportion were alkanes (39.39%) and oxygenated volatile organic compounds (OVOCs, 34.63%). Five VOCs emission sources were determined by the PMF model, of which the largest contribution was from motor vehicle exhaust emission sources (33.10%), followed by industrial emission sources (29.46%), natural gas and coal combustion sources (17.31%), solvent use sources (11.94%), and plant emission sources (8.19%). Controlling motor vehicle exhaust emission sources is the key to alleviate VOCs pollution in summer in Yuncheng City. The average ozone formation potential (OFP) of VOCs was 162.88 μg·m-3, in which OVOCs had the highest contribution rate (45.37%); acetaldehyde, propionaldehyde, ethylene, isoprene, and toluene were the key active components; and industrial emission sources were the emission sources with the highest contribution rate. The average value of secondary organic aerosol formation potential (SOAp) of VOCs was 0.40 μg·m-3, in which the contribution rate of aromatic hydrocarbons was the highest (88.00%), and the solvent use source was the emission source with the highest contribution rate.

Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction

Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful life (RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions.

An efficient online outlier recognition method of dam monitoring data based on improved M-robust regression

Common anomaly recognition methods are easy to misjudge and miss outliers for the online monitoring data. This is a bottleneck problem that needs to be overcome in dam safety management moving toward informatization. Based on the data of nine hydropower stations along Dadu River Basin, this paper analyzed existing problems of the common anomaly identification method and an algorithm was proposed based on improved M-robust regression recognition. In this algorithm, the AR factor was introduced to avoid the defect that the traditional model cannot simulate random variables. The extreme value method and robust estimation were utilized to avoid the leverage effect. The model collapse caused by maximum measured value was avoided through improving the residual calculation model of M-robust and optimizing the weight distribution function. The maximum of the three values, residual quartile difference, discrete quartile difference, and measurement accuracy, was used as an anomaly recognition criterion to improve the evaluation criteria. The algorithm compiled was used in the Dadu River Company since 2017. The statistics showed that for the 150,000 measured values per day, the evaluation time could be within 15 min, the missed judgment rate was 0%, and the misjudgment rate was less than 2%. The proposed algorithm achieved a great improvement and can meet the needs of online outlier recognition in dam safety management.

Research on on-line monitoring data cleaning of transformer oil chromatogram based on machine learning and neural networks

Transformer oil chromatogram on-line monitoring device is a widely used sensing device to monitor the operation state of transformer. Due to the lack of hardware verification means, the on-line chromatographic data generally has the problem of drift. In actual operation, it is mostly used as the basis for trend judgment, but can not reflect the effective value of chromatographic measurement. In this paper, the LSTM neural networks is used to correct the on-line monitoring data of transformer oil chromatogram. The off-line test data combined with neural networks data cleaning technology are used to repeatedly feed back and calibrate the online data, so that the corrected chromatographic on-line number is steadily close to the off-line detection data, which greatly improves the availability and accuracy of on-line chromatographic data. The research results of this paper have important reference significance for the data cleaning of on-line monitoring and measurement of electrical equipment.

Assessment of PM2.5-related health effects: A comparative study using multiple methods and multi-source data in China

In China, PM2.5 pollution has caused extensive death and economic loss. Thus, an accurate assessment of the spatial distribution of these losses is crucial for delineating priority areas for air pollution control in China. In this study, we assessed the PM2.5 exposure-related health effects according to the integrated exposure risk function and non-linear power law (NLP) function in 338 prefecture-level cities in China by utilizing online monitoring data and the PM2.5 Hindcast Database (PHD). Our results revealed no significant difference between the monitoring data and PHD (p value = 0.66 > 0.05). The number of deaths caused by PM2.5-related Stroke (cerebrovascular disease), ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer at the national level estimated through the NLP function was 0.27 million (95% CI: 0.06–0.50), 0.23 million (95% CI: 0.08–0.38), 0.31 million (95% CI: 0.04–0.57), and 0.31 million (95% CI: 0.16–0.40), respectively. The total economic cost at the national level in 2016 was approximately US$80.25 billion (95% CI: 24.46–132.25). Based on a comparison of Z statistics, we propose that the evaluation results obtained using the NLP function and monitoring data are accurate. Additionally, according to scenario simulations, Beijing, Chongqing, Tianjin, and other cities should be priority areas for PM2.5 pollution control to achieve considerable health benefits. Our statistics can help improve the accuracy of PM2.5-related health effect assessments in China.

Accurate Identification of Pollution Sources in a Chemical Enterprise Based on a Distributed Multi-channel VOCs Online Monitoring Mass Spectrometry System

A monitoring and traceability management system for unorganized emissions of volatile organic compounds (VOCs) in industrial parks has been established. This system uses distributed multi-channel mass spectrometry to continuously monitor multiple online monitoring points within the enterprise and the factory boundary to initially identify the source of unorganized emissions of VOCs. Based on online monitoring data, the system combined a positive matrix factorization (PMF) model and conditional bivariate probability function (CBPF) methods to accurately identify pollution sources of different scales in the park. The system was successfully applied to a pharmaceutical chemical factory in Taizhou Chemical Industry Park, and VOCs online monitoring was carried out for more than three months at 10 monitoring sites in the factory area. The sources of VOCs were analyzed using the PMF model, and the geographical location information of each pollution source factor was identified by the CBPF method. During the monitoring period, the contribution of chlorobenzene emissions and the number of early warnings were much higher than those of other species. Compared with that of other species, the frequency of abnormal emissions of butene was higher, and the frequency of abnormal emissions of toluene was lower. Among the top ten species, only chlorobenzene had significant concentration changes in different monitoring sites. Ambient VOCs in the plant mainly came from six sources:butene emission, toluene emission, chlorobenzene emission, solvent use, sulfur-related processes, and industrial production. Based on the CBPF results of each pollution source factor relative to the ten monitoring stations, the local pollution source and external pollution source in the plant were distinguished, and the specific location of the local pollution source and the transmission direction of the external pollution source were identified.

Variety of the Composition and Sources of VOCs During the Spring Festival and Epidemic Prevention in the Pearl River Delta

Volatile organic compounds (VOCs) are the key precursors of the ozone (O3) formation processes in the troposphere and are important control objects for the coordinated governance of O3 and PM2.5. The Spring Festival of 2020 was affected by the novel coronavirus (COVID-19) pneumonia epidemic:companies stopped work and production, and traffic was restricted, providing scientific experimentation opportunities for pollutant emission reduction research. This study analyzed the variety of the composition, chemical reaction activity, and sources of VOCs in the Pearl River Delta during the Spring Festival and the epidemic control period, using real-time online monitoring data of VOCs obtained at four sites(Guangzhou, Dongguan, Zhongshan, and Duanfen)in the Pearl River Delta from January 1, 2020 to February 29, 2020. The results showed that during the Spring Festival and the epidemic control period, the average of φ (VOCs) in the Pearl River Delta was 15.89×10-9, and the maximum hourly average concentration was 45.43×10-9, values that were 44% and 60% lower, respectively, than those before the Spring Festival holiday. Among the VOCs component concentration decreases, the aromatic hydrocarbon component decreased the most, and the decrease in the urban area of the Pearl River Delta (74%) was significantly greater than that in the suburban area (56%). As a result, the contribution rate of aromatic hydrocarbons to the total VOCs was reduced to less than 10%. The analysis of the·OH reaction activity of VOCs(L·OH)and ozone formation potential(OFP)showed that the L·OH and OFP of VOCs decreased significantly in the Pearl River Delta during the Spring Festival and the epidemic control period. Compared with those before the Spring Festival holiday, the total L·OH and total OFP decreased by an average of 60% and 63% in the urban area of the Pearl River Delta, respectively. Additionally, the atmospheric oxidation had also been significantly reduced, which showed a 28% decrease in ρ(Ox). The ratio of toluene/benzene showed that the influence of industrial sources had almost disappeared during the Spring Festival and the epidemic control period, and the total points of the representative components of industrial-related solvent-use sources such as toluene, ethylbenzene, and m/p-xylene dropped by 72% to 91%. The results of this study suggest that solvent-use sources and vehicle exhaust emission sources are the current sources of VOCs that need to be paid attention to in the prevention and control of O3 pollution in the Pearl River Delta region, and the impact of petrochemical sources cannot be ignored in the work of further reducing the background concentration of O3.