Spatio-Temporal Characteristics and Variation Pattern of the Atmospheric Particulate Matter Concentration: A Case Study of the Beijing–Tianjin–Hebei Region, China

Based on measurement data from air quality monitoring stations, the spatio-temporal characteristics of the concentrations of particles with aerodynamic equivalent diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively) in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2018 were analysed at yearly, seasonal, monthly, daily and hourly scales. The results indicated that (1) from 2015 to 2018, the annual average values of PM2.5 and PM10 concentrations and the PM2.5/PM10 ratio in the study area decreased each year; (2) the particulate matter (PM) concentration in winter was significantly higher than that in summer, and the PM2.5/PM10 ratio was highest in winter and lowest in spring; (3) the PM2.5 and PM10 concentrations exhibited a pattern of double peaks and valleys throughout the day, reaching peak values at night and in the morning and valleys in the morning and afternoon; and (4) with the use of an improved sine function to simulate the change trend of the monthly mean PM concentration, the fitting R2 values for PM2.5 and PM10 in the whole study area were 0.74 and 0.58, respectively. Moreover, the high-value duration was shorter, the low-value duration was longer, and the concentration decrease rate was slower than the increase rate.

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Quantification of Non-Exhaust Particulate Matter Traffic Emissions and the Impact of COVID-19 Lockdown at London Marylebone Road

Atmosphere ◽

10.3390/atmos12020190 ◽

2021 ◽

Vol 12 (2) ◽

pp. 190

Author(s):

William Hicks ◽

Sean Beevers ◽

Anja H. Tremper ◽

Gregor Stewart ◽

Max Priestman ◽

...

Keyword(s):

Particulate Matter ◽

Measurement Data ◽

Emission Factors ◽

Meteorological Conditions ◽

Traffic Emissions ◽

Atmospheric Particulate Matter ◽

Exhaust Emission ◽

Brake Wear ◽

The Impact ◽

Exhaust Particulate

This research quantifies current sources of non-exhaust particulate matter traffic emissions in London using simultaneous, highly time-resolved, atmospheric particulate matter mass and chemical composition measurements. The measurement campaign ran at Marylebone Road (roadside) and Honor Oak Park (background) urban monitoring sites over a 12-month period between 1 September 2019 and 31 August 2020. The measurement data were used to determine the traffic increment (roadside–background) and covered a range of meteorological conditions, seasons, and driving styles, as well as the influence of the COVID-19 “lockdown” on non-exhaust concentrations. Non-exhaust particulate matter (PM)10 concentrations were calculated using chemical tracer scaling factors for brake wear (barium), tyre wear (zinc), and resuspension (silicon) and as average vehicle fleet non-exhaust emission factors, using a CO2 “dilution approach”. The effect of lockdown, which saw a 32% reduction in traffic volume and a 15% increase in average speed on Marylebone Road, resulted in lower PM10 and PM2.5 traffic increments and brake wear concentrations but similar tyre and resuspension concentrations, confirming that factors that determine non-exhaust emissions are complex. Brake wear was found to be the highest average non-exhaust emission source. In addition, results indicate that non-exhaust emission factors were dependent upon speed and road surface wetness conditions. Further statistical analysis incorporating a wider variability in vehicle mix, speeds, and meteorological conditions, as well as advanced source apportionment of the PM measurement data, were undertaken to enhance our understanding of these important vehicle sources.

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Concentração de Material Particulado (PM10) na Região de Tangará da Serra-MT, Sul da Amazônia Legal (Particulate Matter Concentration in Tangará da Serra Region, Southern Legal...)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v7.6.p1145-1152 ◽

2015 ◽

Vol 7 (6) ◽

pp. 1145

Author(s):

Patricia Simone Palhana Moreira ◽

Rivanildo Dallacort ◽

Idilaine De Fatima Lima ◽

Rafael Cesar Tieppo ◽

Cristiano Santos

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Meteorological Data ◽

Average Concentration ◽

Atmospheric Particulate Matter ◽

Meteorological Variables ◽

State University ◽

Mato Grosso ◽

Short Period ◽

Matter Concentration

O objetivo do presente trabalho foi analisar as concentrações de material particulado presente na atmosfera de Tangará da Serra-MT, e correlacioná-los com as variáveis meteorológicas, informações de saúde e com o número de focos de queimada no Estado de Mato Grosso. Os dados de material particulado foram amostrados diariamente a cada 5 minutos, com auxilio do coletor DataRam4, no período de agosto de 2008 a julho de 2009. Os dados meteorológicos foram disponibilizados pelo Instituto Nacional de Meteorologia - INMET, o qual possui uma estação meteorológica instalada na Universidade do Estado de Mato Grosso – UNEMAT. A média de concentração do período foi de 30,1 ug.m-3. Os meses de agosto, setembro e outubro apresentaram concentrações mais altas de material particulado, nestes meses também ocorreram os maiores números de queimadas no Estado. Nos meses em que foram registrados os picos de concentração, houve dias em que os padrões de qualidade do ar foram ultrapassados. No mês de outubro, que foi o de maior concentração, as médias diárias ultrapassaram 150 ug.m-3 em três dias. As concentrações de material particulado (PM10) foram altas apenas em um período relativamente curto, de apenas três meses, nos demais meses as concentrações foram baixas, não ultrapassando os limites de qualidade do ar. A B S T R A C T The aim of this work was to analyze the atmospheric particulate matter concentrations in Tangara da Serra MT, and correlate them with meteorological variables, health information and the number of fire spots in Mato Grosso State. The particulate matter data were sampled every five minutes daily with a DataRam4 collector, from August 2008 to July 2009. Meteorological data were acquired from the National Institute of Meteorology - INMET, which has a weather station at the Mato Grosso State University - UNEMAT. The average concentration for the period was 30.1 ug.m-3. The months of August, September and October showed higher concentrations of particulate matter, in these months also occurred the highest number of fire spots in the State. In the months that had the concentrations peak, there were days when the air quality standards were exceeded. In October, which had the highest concentration, the daily average exceeded 150 ug.m-3 in three days. The concentrations of particulate matter (PM10) were high, but only in a relatively short period of three months, in the remaining months the concentrations were low, not exceeding the limits of air quality. Keywords: Meteorological Variables, Fire Spots, Meteorology.

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Comparison of Low-Cost Particulate Matter Sensors for Indoor Air Monitoring during COVID-19 Lockdown

Sensors ◽

10.3390/s20247290 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7290

Author(s):

Miron Kaliszewski ◽

Maksymilian Włodarski ◽

Jarosław Młyńczak ◽

Krzysztof Kopczyński

Keyword(s):

Particulate Matter ◽

Low Cost ◽

Air Monitoring ◽

Concentration Estimation ◽

Air Purifier ◽

Pm2.5 And Pm10 ◽

Indoor Conditions ◽

The Relationship ◽

Beijing China ◽

Matter Concentration

This study shows the results of air monitoring in high- and low-occupancy rooms using two combinations of sensors, AeroTrak8220(TSI)/OPC-N3 (AlphaSense, Great Notley, UK) and OPC-N3/PMS5003 (Plantower, Beijing, China), respectively. The tests were conducted in a flat in Warsaw during the restrictions imposed due to the COVID-19 lockdown. The results showed that OPC-N3 underestimates the PN (particle number concentration) by about 2–3 times compared to the AeroTrak8220. Subsequently, the OPC-N3 was compared with another low-cost sensor, the PMS5003. Both devices showed similar efficiency in PN estimation, whereas PM (particulate matter) concentration estimation differed significantly. Moreover, the relationship among the PM1–PM2.5–PM10 readings obtained with the PMS5003 appeared improbably linear regarding the natural indoor conditions. The correlation of PM concentrations obtained with the PMS5003 suggests an oversimplified calculation method of PM. The studies also demonstrated that PM1, PM2.5, and PM10 concentrations in the high- to low-occupancy rooms were about 3, 2, and 1.5 times, respectively. On the other hand, the use of an air purifier considerably reduced the PM concentrations to similar levels in both rooms. All the sensors showed that frying and toast-making were the major sources of particulate matter, about 10 times higher compared to average levels. Considerably lower particle levels were measured in the low-occupancy room.

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