scholarly journals THE CHARACTERISTICS AND POTENTIAL SOURCE AREA ANALYSIS OF PM<sub>2.5</sub> CONCENTRATION FOR ZHENGZHOU DURING 2016

2018 ◽  
Vol XLII-3 ◽  
pp. 781-787
Author(s):  
D. Li ◽  
J. Liu ◽  
S. Li ◽  
C. Wang ◽  
S. Zhou
Keyword(s):  
Meteorological Data ◽  
Source Area ◽  
Pollution Monitoring ◽  
Air Mass ◽  
Double Peak Structure ◽  
Air Pollution Monitoring ◽  
Source Areas ◽  
Peak Structure ◽  
Potential Source ◽  
Air Mass Trajectory

This study used the HYSPLIT-4 model combined with cluster analysis, potential source pollution contribution functions and other methods to analyse the ground air pollution monitoring data and meteorological data in Zhengzhou during 2016. The results showed that: 1) the level of PM<sub>2.5</sub> reached the low value in summer. The PM<sub>2.5</sub> concentration reached the highest level in December and reached the lowest level in August. The daily variation characteristics of PM<sub>2.5</sub> concentration in different seasons were roughly the same, and it had an obviously "double-peak" structure. 2) The annual PM<sub>2.5</sub> concentration was positively correlated with atmospheric pressure and relative humidity. The annual PM<sub>2.5</sub> concentration was negatively correlated with temperature, visibility, precipitation, and wind speed. 3) In winter, the air mass trajectory that through the northern Sichuan &amp;ndash; Gansu &amp;ndash; Shaanxi &amp;ndash; Hubei was polluted seriously, and the level of PM<sub>2.5</sub> was the highest which reached to 202.13&amp;thinsp;μg/m<sup>3</sup>. In summer, the air mass trajectory that came from Hubei was the lowest level with the value is 40.17&amp;thinsp;μg/m<sup>3</sup>. 4) The potential source areas located in northwest of Zhengzhou, Gansu, Hubei and Beijing-Tianjin-Hebei region in spring. The surrounding of Zhengzhou contributed to the pollution of Zhengzhou. The potential source areas appeared in Shaanxi, Sichuan, and Qinghai, the border between Ningxia and Inner Mongolia in autumn. In winter the potential source areas located in Jiangsu, Hubei, Henan, eastern of Shanxi, southern of Shanxi, Ningxia and the area of Yellow Sea, etc.

scholarly journals Identifying Key Potential Source Areas for Ambient Methyl Mercaptan Pollution Based on Long-Term Environmental Monitoring Data in an Industrial Park

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 501 ◽  
Author(s):  
Yujie Liu ◽  
Qi Yu ◽  
Zihan Huang ◽  
Weichun Ma ◽  
Yan Zhang
Keyword(s):  
Source Identification ◽  
Meteorological Data ◽  
Source Area ◽  
Industrial Park ◽  
Methyl Mercaptan ◽  
Source Areas ◽  
Potential Source ◽  
Industrial Parks ◽  
Concentrated Source

Precise source identification for ambient pollution incidents in industrial parks were often difficult due to limited measurements. Source area analysis method was one of the applicable source identification methods, which could provide potential source areas under these circumstances. However, a source area usually covered several sources and the method was unable to identify the real one. This article introduces a case study on the statistical source identification of methyl mercaptan based on the long-term measurements, in 2014, in an industrial park. A procedure for statistical source area analysis was established, which contains independent pollution episode extraction, source area calculation scenario definition, meteorological data selection, and source area statistical analysis. A total of 414 violation records were detected by five monitors inside the park. Three kinds of calculation scenarios were found and, finally, three key source areas were revealed. The typical scenarios of source area calculations were described in detail. The characteristics of the statistical source areas for all pollution episodes were examined. Finally, the applicability of the method, as well as the source of uncertainties, was discussed. This study shows that more concentrated source areas can be identified through the statistical source area method if several excessive emission sources exist in an industrial park.

Observed surface ozone trend in the year 2012 over Nairobi, Kenya

Atmósfera ◽  
2015 ◽  
Vol 27 (4) ◽  
pp. 377-384
Author(s):  
ZABLON W. SHILENJE ◽  
VICTOR ONGOMA
Keyword(s):  
Meteorological Data ◽  
Surface Ozone ◽  
Daily Basis ◽  
Meteorological Station ◽  
Pollution Monitoring ◽  
Monthly Basis ◽  
Air Pollution Monitoring ◽  
Living Things ◽  
The Times ◽  
One Year

Clean air is a basic requirement for human health and wellbeing. The Kenya Meteorological Department has established air pollution monitoring activities in various sites in Nairobi, at Dagoretti Corner meteorological station and at Mount Kenya. Different pollutants are measured including ozone. The increased concentration of greenhouse gases in the atmosphere has influenced the weather and climate. This study examined the variations of surface ozone over Dagoretti Corner, Nairobi for a 12-month period ending July 2013, exactly one year after the start of data acquisition. The trend was studied using time series analysis of ozone concentration on both an hourly and monthly basis. The ozone data was then combined with meteorological data and temperature to find correlations between the two. Overall, the air quality of Nairobi, represented by Dagoretti Corner meteorological station is good as compared to the World Meteorological Organization ozone standards. The highest concentration of ozone is observed in the afternoon and the minimum at dawn on a daily basis. On seasonal scale, the highest levels are recorded in the cold months. This information helps to reduce exposure to the gas and thus to reduce its impacts on living things. The study recommends the reduction of exposure to the gas during the times when it has been observed to be highest in order to minimize its impacts

MULTI-SITE AEROSOL MONITORING USING MINI STEP SAMPLER

2003 ◽  
Vol 13 (01n02) ◽  
pp. 65-80 ◽  
Author(s):  
S. Matsuyama ◽  
K. Katoh ◽  
S. Sugihara ◽  
K. Ishii ◽  
H. Yamazaki ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Pollution Monitoring ◽  
Aerosol Sampling ◽  
Sampling System ◽  
Night Time ◽  
Simultaneous Exposure ◽  
Air Pollution Monitoring ◽  
Elemental Concentrations ◽  
Sources Of Pollution ◽  
Suction Nozzle

We developed mini step samplers with low manufacturing and running costs for application in multi-site air-pollution monitoring. The miniaturization of the sampler was achieved by reducing the suction nozzle size. We tested the samplers with suction nozzle diameters of 2 and 4 mm through simultaneous exposure in the same site. Elemental concentrations of aerosol collected by these samplers were consistent within ±20% during comparison and the sample uniformity did not differ significantly. Sampling with small suction nozzle did not adversely affect aerosol collection. Aerosol samples were collected simultaneously at two sites in our laboratory and in the hall outside for 3 days and analyzed subsequently by PIXE. The time variation of elemental concentrations was high during daytime and low at night time and also during the weekend. Elemental concentrations in the hall were always higher than those in the laboratory. In our laboratory, we change shoes at the entrance and therefore, elemental concentrations inside the lab are lower than in the hall. In a second field experiment, we carried out simultaneous multi-site aerosol sampling during two periods in correlation with meteorological data (wind direction and velocity). It was observed that elemental concentrations of some soil origin elements changed periodically. On the other hand, the concentration of Cu and Zn showed irregular concentration spikes whose pattern showed a variation with the sites. Analysis using the data of wind directions showed that Cu had been transported to the sites from northeasterly direction and that the concentration of Zn was influenced by two big factories nearby. In conclusion, it has been demonstrated that the multi-site sampling system combined with meteorological data is well suited to identify sources of pollution.

scholarly journals Temporal Variations and Potential Source Areas of Fine Particulate Matter in Bangkok, Thailand

2020 ◽  
Vol 13 ◽  
pp. 117862212097820
Author(s):  
Suwimon Kanchanasuta ◽  
Sirapong Sooktawee ◽  
Aduldech Patpai ◽  
Pisit Vatanasomboon
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Source Area ◽  
Temporal Variations ◽  
Statistical Technique ◽  
Monitoring Station ◽  
Polar Plot ◽  
Source Areas ◽  
Low Wind Speed ◽  
Potential Source

Particulate matter (PM) less than 2.5 micron (PM2.5) issue is 1 of the important targets of concern by the United Nations’ Sustainable Development Goals. Bangkok is a megacity and facing air pollution problems. This study analyzed PM, PM2.5 and PM less than 10 micron (PM10), monitoring data from stations located in Bangkok, and aimed to present their variations in diurnal, weekly, and intra-annual timescales. High PM concentrations are related to calm wind. The diurnal variation of PM2.5/PM10 suggested a greater accumulation of PM2.5 than PMcoarse during the low wind speed. Potential source areas affecting PM rising at each monitoring station were identified using statistical technique, bivariate polar plot, and conditional bivariate probability function. Results showed that Ratchathewi District Monitoring Station identified 3 potential source areas related to emissions from transportation sources creating rising PM concentrations. The first potential source was located in the northwest direction, namely, the Rama VI Road close to the conjunction with Ratchawithi Road. The second potential source area was located around the cross-section between Phaya Thai Road and Rama I Road, while the third was located at the intersection of the Phaya Thai Road to Yothi Street and Rang Nam Road. These potential source areas constitute useful information for managing and reducing PM.

A COMPARATIVE ANALYSIS AND REVIEW OF VARIOUS AIR POLLUTION MONITORING SCHEMES

2016 ◽  
Vol 5 (1) ◽  
pp. 30
Author(s):  
HASAN MOHD. TAHSEENUL ◽  
CHOURASIA VIJAY S. ◽  
ASUTKAR SANJAY M. ◽  
◽  
◽  
...  
Keyword(s):  
Air Pollution ◽  
Comparative Analysis ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring

scholarly journals H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Raw Data ◽  
Air Pollution Monitoring

scholarly journals A Study on Indoor Particulate Matter Variation in Time Based on Count and Sizes and in Relation to Meteorological Conditions

2021 ◽  
Vol 13 (15) ◽  
pp. 8263
Author(s):  
Marius Bodor
Keyword(s):  
Particulate Matter ◽  
Meteorological Data ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Indoor Location ◽  
Particle Matter ◽  
Indoor Particulate Matter

An important aspect of air pollution analysis consists of the varied presence of particulate matter in analyzed air samples. In this respect, the present work aims to present a case study regarding the evolution in time of quantified particulate matter of different sizes. This study is based on data acquisitioned in an indoor location, already used in a former particulate matter-related article; thus, it can be considered as a continuation of that study, with the general aim to demonstrate the necessity to expand the existing network for pollution monitoring. Besides particle matter quantification, a correlation of the obtained results is also presented against meteorological data acquisitioned by the National Air Quality Monitoring Network. The transformation of quantified PM data in mass per volume and a comparison with other results are also addressed.

Research on air pollution system based on neural network

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Image Features ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
System Model ◽  
Process Data ◽  
Deep Convolutional Neural Networks ◽  
Air Pollution Monitoring ◽  
Neural Network Algorithm

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.

IoT Enabled an Open Framework for Air Pollution Monitoring System

2020 ◽  
Author(s):  
B.H. Sudantha ◽  
Manchanayaka MALSK ◽  
Nilantha Premakumara ◽  
Chamani Shiranthika ◽  
C. Premachandra ◽  
...  
Keyword(s):  
Air Pollution ◽  
Monitoring System ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Open Framework

scholarly journals Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

Sign in / Sign up

Export Citation Format

Share Document