scholarly journals Effect of the COVID-19 Lockdown on Air Pollution in the Ostrava Region

2021 ◽  
Vol 18 (16) ◽  
pp. 8265
Author(s):  
Jan Bitta ◽  
Vladislav Svozilík ◽  
Aneta Svozilíková Svozilíková Krakovská
Keyword(s):  
Meteorological Data ◽  
Traffic Monitoring ◽  
Meteorological Conditions ◽  
Pollution Monitoring ◽  
Monitoring Station ◽  
Traffic Intensity ◽  
Weather Patterns ◽  
Proper Estimation ◽  
Pollution Concentrations ◽  
Monitoring Stations

A proper estimation of anti-epidemic measures related to the influence of the COVID-19 outbreak on air quality has to deal with filtering out the weather influence on pollution concentrations. The goal of this study was to estimate the effect of anti-epidemic measures at three pollution monitoring stations in the Ostrava region. Meteorological data were clustered into groups with a similar weather pattern, and pollution data were divided into subsets according to weather patterns. Then each subset was evaluated separately. Our estimates showed a 4.1–5.7% decrease in NOx concentrations attributed to lower traffic intensity during the lockdown. The decrease of PM2.5 varied more significantly between monitoring stations. The highest decrease (4.7%) was detected at the traffic monitoring station, while there was no decrease detected at the rural monitoring station, which focuses mainly on domestic heating pollution. The key result of the study was the development of an analytical method that is able to take into account the effect of meteorological conditions. The method is much simpler and easy to replicate as an alternative to other published methods.

scholarly journals Statistical classification of synoptic weather patterns associated with Tehran air pollution

2020 ◽  
Author(s):  
Sakineh Khansalari ◽  
Nastran Ghobadi ◽  
Abbasali Aliakbari Bidokhti ◽  
Farahnaz Fazel Rastgar
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Meteorological Data ◽  
Principal Component ◽  
Static Stability ◽  
Meteorological Conditions ◽  
Synoptic Patterns ◽  
Weather Patterns ◽  
Concentration Changes ◽  
The Relationship

Introduction: Poor air quality in the heavily polluted cities like Tehran is often the main city problem that influences people health and comfort. The main goals of this study are summarized as: 1) Seasonal pollutants mean variations during 2005, meteorological conditions effects on pollutant concentration; 2) Meteorological conditions case study and pollution spatial distribution for three determining synoptic patterns (MET1, MET4, MET5); 3) Further analysis of the episode from 30th November to 13th December 2005 (MET4); 4) Episode analysis from 30th November to 13th December 2005 (MET4) and 5) Episode analysis from 12th-22th of September 2005 (MET5). These are systematic weather patterns that usually affect the air pollution levels in Tehran. Materials and methods: Concentration changes of CO, PM10, SO2 and O3, as the relationship between the air pollution extreme events and atmospheric conditions in Tehran have been investigated. The hourly air pollution data from 11 representative monitoring sites were used. To understand the relationship between local meteorological synoptic patterns and air pollution, the principal component analysis (PCA) method has been applied to meteorological data. Then for minimizing the data complication the varimax rotations (VR) was used and five synoptic perspectives weather patterns have resulted for highly polluted periods. Results: Pollutants correlation investigation of the five patterns showed that air quality was highly dependent on middle tropospheric high geopotential ridge development, local southerly wind with strong static stability. Conclusion: The most polluted periods were associated with a weak pressure gradient, a weak wind, severe air descent, and radiation inversion.

scholarly journals The impacts of commissioning coal-fired power stations on air quality in South Africa: insights from ambient monitoring stations

2020 ◽  
Vol 30 (2) ◽  
Author(s):  
Itumeleng P. Morosele ◽  
Kristy E. Langerman
Keyword(s):  
South Africa ◽  
Air Quality ◽  
Ambient Air ◽  
Monitoring Station ◽  
Ambient Air Quality ◽  
Power Station ◽  
Power Stations ◽  
Ambient Pollution ◽  
Pollution Concentrations ◽  
Monitoring Stations

The South African electricity sector is known for its heavy reliance on coal. The aim of this study is to assess the impacts of increasing SO2 and PM emissions from the three return-to-service power stations (Komati, Camden and Grootvlei), and the newly constructed Medupi power station on ambient air quality measured in the vicinities of these power stations. Trends in ambient pollution concentrations were determined using Theil-Sen analysis. The correlation between the emissions and ambient pollution concentrations at nearby monitoring stations was determined with the Spearman partial rank correlation coefficient.  Lastly, compliance of ambient pollution concentrations with the South Africa National Ambient Air Quality Standards was assessed. Few statistically significant trends in ambient SO2 and PM10 concentrations are found, and there is little correlation between increasing power station emissions and ambient pollutant concentrations in the vicinity. It is only at Camden monitoring station where there are increases in PM10 concentrations from the direction of Camden power station, and at Grootvlei monitoring station where increasing SO2 concentrations are from the directions of Grootvlei and Lethabo power stations. A strong, positive correlation between power station emissions and ambient concentrations exists only for SO2 at Grootvlei monitoring station and PM10 at Medupi monitoring station (although it is likely that the correlation at Medupi is related to construction and vehicle activity, and not emissions from Medupi power station stacks). It is concluded that the establishment of monitoring stations in the vicinities of power stations is necessary but not sufficient to monitor their impact on air quality in the surrounds.

scholarly journals Rain Erosion Maps for Wind Turbines Based on Geographical Locations: A Case Study in Ireland and Britain

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
K. Pugh ◽  
M. M. Stack
Keyword(s):  
Wind Turbine ◽  
Western Europe ◽  
Meteorological Data ◽  
Turbine Blades ◽  
Weather Conditions ◽  
Annual Rainfall ◽  
Wind Turbine Blades ◽  
Weather Patterns ◽  
The Uk

AbstractErosion rates of wind turbine blades are not constant, and they depend on many external factors including meteorological differences relating to global weather patterns. In order to track the degradation of the turbine blades, it is important to analyse the distribution and change in weather conditions across the country. This case study addresses rainfall in Western Europe using the UK and Ireland data to create a relationship between the erosion rate of wind turbine blades and rainfall for both countries. In order to match the appropriate erosion data to the meteorological data, 2 months of the annual rainfall were chosen, and the differences were analysed. The month of highest rain, January and month of least rain, May were selected for the study. The two variables were then combined with other data including hailstorm events and locations of wind turbine farms to create a general overview of erosion with relation to wind turbine blades.

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.

Air quality at open-cast coal mining regions at Hunter Valley, Australia and Ramagundam, India

2021 ◽  
Author(s):  
Harsh Kamath ◽  
Chanchal Chauhan ◽  
Sameer Mishra ◽  
Aariz Ahmed ◽  
Raman Srikanth
Keyword(s):  
Power Plants ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Thermal Power ◽  
Coal Mines ◽  
Export Market ◽  
P Value ◽  
Open Cast ◽  
Monitoring Stations

<p>The upper Hunter Valley region in New South Wales (NSW), Australia has several open-cast coal mines, which supply coal to two large thermal power plants (TPPs) in the area, beside the export market. Long-term Particulate Matter (PM) pollutants and meteorological measurements are recorded by a network of 13 NSW government-owned continuous monitoring stations in the upper Hunter Valley region. The Ramagundam area in the state of Telangana, India has similar pollution source characteristics (coal mines and TPPs), but PM pollutant measurements are largely carried out with manual monitoring stations at 24-hour intervals, not more than twice a week. As the coal and overburden excavation from open-cast coal mines and stack emissions from TPPs lead to local PM pollution, we have used MODIS-MAIAC Aerosol Optical Depth (AOD) at 550 nm and Normalized Difference Vegetation Index (NDVI) along with the local meteorological data such as ambient temperature, relative humidity, wind speed and direction to model PM10 and PM2.5 at the upper Hunter Valley and Ramagundam regions. Our model can explain about 60% of variation in PM10 (p-value < 0.0001), while a similar model is able to explain about 75% of the variation in the PM2.5 (p-value < 0.0001). We will extend our model results from Hunter Valley to Ramagundam area and comment on the potential of using geospatial products such as AOD as a proxy to ground-based pollution measurements in developing countries such as India, where pollution data is scarce.</p>

scholarly journals Inter-community variability in total particle number concentrations in the eastern Los Angeles air basin

2010 ◽  
Vol 10 (23) ◽  
pp. 11385-11399 ◽  
Author(s):  
N. Hudda ◽  
K. Cheung ◽  
K. F. Moore ◽  
C. Sioutas
Keyword(s):  
Los Angeles ◽  
Spatial Variability ◽  
Spatial Variation ◽  
Urban Areas ◽  
Particle Number ◽  
Meteorological Conditions ◽  
Monitoring Station ◽  
Size Distributions ◽  
Central Monitoring ◽  
Seasonal And Diurnal Variations

Abstract. Ultrafine Particles (UFP) can display sharp gradients in their number concentrations in urban environment due to their transient nature and rapid atmospheric processing. The ability of using air pollution data generated at a central monitoring station to assess exposure relies on our understanding of the spatial variability of a specific pollutant associated with a region. High spatial variation in the concentrations of air pollutants has been reported at scales of 10s of km for areas affected by primary emissions. Spatial variability in particle number concentrations (PNC) and size distributions needs to be investigated, as the representativeness of a monitoring station in a region is premised on the assumption of homogeneity in both of these metrics. This study was conducted at six sites, one in downtown Los Angeles and five located about 40–115 km downwind in the receptor areas of Los Angeles air basin. PNC and size distribution were measured using Condensation Particle Counters (CPC) and Scanning Mobility Particle Sizer (SMPS). The seasonal and diurnal variations of PNC implied that PNC might vary significantly with meteorological conditions, even though the general patterns at the sites may remain generally similar across the year due to consistency of sources around them. Regionally transported particulate matter (PM) from upwind urban areas of Los Angeles lowered spatial variation by acting as a "homogenizing" factor during favorable meteorological conditions. Spatial variability also increased during hours of the day during which the effects of local sources predominate. The spatial variability associated with PNC (quantified using Coefficients of Divergence, CODs), averaged about 0.3, which was generally lower than that based on specific size ranges. Results showed an inverse relationship of COD with particles size, with fairly uniform values in the particle range which is associated with regional transport. Our results suggest that spatial variability, even in the receptor regions of Los Angeles Basin, should be assessed for both PNC and size distributions, and should be interpreted in context of seasonal and diurnal influences, and suitably factored if values for exposure are ascertained using a central monitoring station.

scholarly journals Occurrence and growth of sub-50 nm aerosol particles in the Amazonian boundary layer

2021 ◽  
Author(s):  
Marco A. Franco ◽  
Florian Ditas ◽  
Leslie Ann Kremper ◽  
Luiz A. T. Machado ◽  
Meinrat O. Andreae ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Meteorological Data ◽  
Cloud Condensation Nuclei ◽  
Deep Convection ◽  
Potential Temperature ◽  
Meteorological Conditions ◽  
Subsequent Growth ◽  
Wet Season ◽  
Particle Injection ◽  
Continuous Growth

Abstract. New particle formation (NPF), referring to the nucleation of molecular clusters and their subsequent growth into the cloud condensation nuclei (CCN) size range, is a globally significant and climate-relevant source of atmospheric aerosols. Classical NPF exhibiting continuous growth from a few nanometers to the Aitken mode around 60–70 nm is widely observed in the planetary boundary layer (PBL) around the world, but not in central Amazonia. Here, classical NPF events are rarely observed in the PBL, but instead, NPF begins in the upper troposphere (UT), followed by downdraft injection of sub-50 nm (CN< 50) particles into the PBL and their subsequent growth. Central aspects of our understanding of these processes in the Amazon have remained enigmatic, however. Based on more than six years of aerosol and meteorological data from the Amazon Tall Tower Observatory (ATTO, Feb 2014 to Sep 2020), we analyzed the diurnal and seasonal patterns as well as meteorological conditions during 254 of such Amazonian growth events on 217 event days, which show a sudden occurrence of particles between 10 and 50 nm in the PBL, followed by their growth to CCN sizes. The occurrence of events was significantly higher during the wet season, with 88 % of all events from January to June, than during the dry season, with 12 % from July to December, probably due to differences in the condensation sink (CS), atmospheric aerosol load, and meteorological conditions. Across all events, a median growth rate (GR) of 5.2 nm h−1 and a median CS of 0.0011 s−1 were observed. The growth events were more frequent during the daytime (74 %) and showed higher GR (5.9 nm h−1) compared to nighttime events (4.0 nm h−1), emphasizing the role of photochemistry and PBL evolution in particle growth. About 70 % of the events showed a negative anomaly of the equivalent potential temperature (∆θ'e) – as a marker for downdrafts – and a low satellite brightness temperature (Tir) – as a marker for deep convective clouds – in good agreement with particle injection from the UT in the course of strong convective activity. About 30 % of the events, however, occurred in the absence of deep convection, partly under clear sky conditions, and with a positive ∆θ'e anomaly. Therefore, these events do not appear to be related to downdraft injection and suggest the existence of other currently unknown sources of the sub-50 nm particles.

scholarly journals LONG-TERM MARINE TRAFFIC MONITORING FOR ENVIRONMENTAL SAFETY IN THE AEGEAN SEA

2015 ◽  
Vol XL-7/W3 ◽  
pp. 949-952 ◽  
Author(s):  
T. Giannakopoulos ◽  
S. Gyftakis ◽  
E. Charou ◽  
S. Perantonis ◽  
Z. Nivolianitou ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Aegean Sea ◽  
Spatiotemporal Analysis ◽  
Environmental Safety ◽  
Traffic Monitoring ◽  
The Black Sea ◽  
Marine Traffic ◽  
Graphical Modelling ◽  
Pass Through

The Aegean Sea is characterized by an extremely high marine safety risk, mainly due to the significant increase of the traffic of tankers from and to the Black Sea that pass through narrow straits formed by the 1600 Greek islands. Reducing the risk of a ship accident is therefore vital to all socio-economic and environmental sectors. This paper presents an online long-term marine traffic monitoring work-flow that focuses on extracting aggregated vessel risks using spatiotemporal analysis of multilayer information: vessel trajectories, vessel data, meteorological data, bathymetric / hydrographic data as well as information regarding environmentally important areas (e.g. protected high-risk areas, etc.). A web interface that enables user-friendly spatiotemporal queries is implemented at the frontend, while a series of data mining functionalities extracts aggregated statistics regarding: (a) marine risks and accident probabilities for particular areas (b) trajectories clustering information (c) general marine statistics (cargo types, etc.) and (d) correlation between spatial environmental importance and marine traffic risk. Towards this end, a set of data clustering and probabilistic graphical modelling techniques has been adopted.

scholarly journals Strategies of method selection for fine-scale PM<sub>2.5</sub> mapping in an intra-urban area using crowdsourced monitoring

2019 ◽  
Vol 12 (5) ◽  
pp. 2933-2948 ◽  
Author(s):  
Shan Xu ◽  
Bin Zou ◽  
Yan Lin ◽  
Xiuge Zhao ◽  
Shenxin Li ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Area ◽  
Significant Risk ◽  
Pollution Monitoring ◽  
Fine Scale ◽  
Method Selection ◽  
Sampling Campaign ◽  
Selection For ◽  
Monitoring Stations

Abstract. Fine particulate matter (PM2.5) is of great concern to the public due to its significant risk to human health. Numerous methods have been developed to estimate spatial PM2.5 concentrations in unobserved locations due to the sparse number of fixed monitoring stations. Due to an increase in low-cost sensing for air pollution monitoring, crowdsourced monitoring of exposure control has been gradually introduced into cities. However, the optimal mapping method for conventional sparse fixed measurements may not be suitable for this new high-density monitoring approach. This study presents a crowdsourced sampling campaign and strategies of method selection for 100 m scale PM2.5 mapping in an intra-urban area of China. During this process, PM2.5 concentrations were measured by laser air quality monitors through a group of volunteers during two 5 h periods. Three extensively employed modelling methods (ordinary kriging, OK; land use regression, LUR; and regression kriging, RK) were adopted to evaluate the performance. An interesting finding is that PM2.5 concentrations in micro-environments varied in the intra-urban area. These local PM2.5 variations can be easily identified by crowdsourced sampling rather than national air quality monitoring stations. The selection of models for fine-scale PM2.5 concentration mapping should be adjusted according to the changing sampling and pollution circumstances. During this project, OK interpolation performs best in conditions with non-peak traffic situations during a lightly polluted period (holdout validation R2: 0.47–0.82), while the RK modelling can perform better during the heavily polluted period (0.32–0.68) and in conditions with peak traffic and relatively few sampling sites (fewer than ∼100) during the lightly polluted period (0.40–0.69). Additionally, the LUR model demonstrates limited ability in estimating PM2.5 concentrations on very fine spatial and temporal scales in this study (0.04–0.55), which challenges the traditional point about the good performance of the LUR model for air pollution mapping. This method selection strategy provides empirical evidence for the best method selection for PM2.5 mapping using crowdsourced monitoring, and this provides a promising way to reduce the exposure risks for individuals in their daily life.

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