Assessment of source contribution to air quality in an urban area close to a harbor: Case-study in Porto, Portugal

Sorte, Sandra; Arunachalam, Saravanan; Naess, Brian; Seppanen, Catherine; Rodrigues, Véra; Valencia, Alejandro; Borrego, Carlos; Monteiro, A.

doi:10.1016/j.scitotenv.2019.01.185

Cited by 43 publications

(22 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, there is a lack of accessible tools that can be easily applied to study near-source pollution and identify contributing sources and develop strategies for reducing emissions and exposure. Several studies have tackled the contribution of various sources to the local air quality using different approaches [8,9]. Monitoring-based assessments can characterize air quality in a community when enough monitors are deployed.…”

Section: Introductionmentioning

confidence: 99%

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

et al. 2019

Self Cite

View full text Add to dashboard Cite

Spatially and temporally resolved air quality characterization is critical for community-scale exposure studies and for developing future air quality mitigation strategies. Monitoring-based assessments can characterize local air quality when enough monitors are deployed. However, modeling plays a vital role in furthering the understanding of the relative contributions of emissions sources impacting the community. In this study, we combine dispersion modeling and measurements from the Kansas City TRansportation local-scale Air Quality Study (KC-TRAQS) and use data fusion methods to characterize air quality. The KC-TRAQS study produced a rich dataset using both traditional and emerging measurement technologies. We used dispersion modeling to support field study design and analysis. In the study design phase, the presumptive placement of fixed monitoring sites and mobile monitoring routes have been corroborated using a research screening tool C-PORT to assess the spatial and temporal coverage relative to the entire study area extent. In the analysis phase, dispersion modeling was used in combination with observations to help interpret the KC-TRAQS data. We extended this work to use data fusion methods to combine observations from stationary, mobile measurements, and dispersion model estimates.

show abstract

Section: Introductionmentioning

confidence: 99%

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among them, the long-period (approximately 40 days) factors included dry bulk loading and unloading operations (which may last for several weeks). Sorte et al [ 16 ] reported that onshore emission sources, such as trucks, railways, cargo handling equipment, and bulk materials, are the primary contributors of PM 10 , and these emission sources can contribute about 80% of the PM 10 concentration in a port. Pérez et al [ 22 ] concluded that the PM emitted by port activities contributed about 50–55% of PM 10 in the harbor.…”

Section: Resultsmentioning

confidence: 99%

“…To date, studies of PM 10 concentrations in ports have mainly concentrated on quantifying the impact on the ambient air quality [ 16 , 17 , 18 ]. By comparing the average value of the PM 10 concentration measured in the ports within a specific time frame, some researchers have discovered that the PM 10 concentration level has distinct seasonal variation characteristics; however, the variation is not uniform in different regions [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Time–Frequency Analysis of Particulate Matter (PM10) Concentration in Dry Bulk Ports Using the Hilbert–Huang Transform

Feng

Shen

Yang

et al. 2020

IJERPH

View full text Add to dashboard Cite

To analyze the time–frequency characteristics of the particulate matter (PM10) concentration, data series measured at dry bulk ports were used to determine the contribution of various factors during different periods to the PM10 concentration level so as to support the formulation of air quality improvement plans around port areas. In this study, the Hilbert–Huang transform (HHT) method was used to analyze the time–frequency characteristics of the PM10 concentration data series measured at three different sites at the Xinglong Port of Zhenjiang, China, over three months. The HHT method consists of two main stages, namely, empirical mode decomposition (EMD) and Hilbert spectrum analysis (HSA), where the EMD technique is used to pre-process the HSA in order to determine the intrinsic mode function (IMF) components of the raw data series. The results show that the periods of the IMF components exhibit significant differences, and the short-period IMF component provides a modest contribution to all IMF components. Using HSA technology for these IMF components, we discovered that the variations in the amplitude of the PM10 concentration over time and frequency are discrete, and the range of this variation is mainly concentrated in the low-frequency band. We inferred that long-term influencing factors determine the PM10 concentration level in the port, and short-term influencing factors determine the difference in concentration data at different sites. Therefore, when formulating PM10 emission mitigation strategies, targeted measures must be implemented according to the period of the different influencing factors. The results of this study can help guide recommendations for port authorities when formulating the optimal layout of measurement devices.

show abstract

“…Besides, since the real-time analysis of the PM chemical components is challenging [41], we cannot analyze the composition and the source of the PM at different sites by UAV-based measurement. However, previous research has shown that PM10 levels in ports mainly relates to traffic [42], with land-based emissions contributing~80% of surface PM10 concentrations in these settings [43]. Table 3 shows PM10 and PM2.5 concentration data have a strong linear correlation, and Figure 7 shows PM2.5 accounted for~80%-90% of PM10 throughout the dry bulk port area of ZJP.…”

Section: Site1mentioning

confidence: 86%

Vertical Distribution of Particulates within the Near-Surface Layer of Dry Bulk Port and Influence Mechanism: A Case Study in China

et al. 2019

View full text Add to dashboard Cite

Knowing the vertical distribution of ambient particulate matter (PM) will help port authorities choose the optimal dust-suppression measures to reduce PM concentrations. In this study, we used an unmanned aerial vehicle (UAV) to assess the vertical distribution (0-120 m altitude) of PM in a dry bulk port along the Yangtze River, China. Total suspended particulates (TSP), PM10, and PM2.5 concentrations at different altitudes were measured at seven sites representing different cargo-handling sites and a background site. Variations in results across sites make it not suitable to characterize the vertical distribution of PM concentration at this port using simple representative distributions. Bulk cargo particle size, fog cannon use, and porous fence all affected the vertical distribution of TSP concentrations but had only minor impacts on PM10 and PM2.5 concentrations. Optimizing porous fence layout according to weather conditions and cargo demand at port have the most potential for mitigating PM pollution related to port operation. As ground-based stations cannot fully measure vertical PM distributions, our methods and results represent an advance in assessing the impact of port activities on air quality and can be used to determine optimal dust-suppression measures for dry bulk ports.

show abstract

Assessment of source contribution to air quality in an urban area close to a harbor: Case-study in Porto, Portugal

Cited by 43 publications

References 37 publications

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

Time–Frequency Analysis of Particulate Matter (PM10) Concentration in Dry Bulk Ports Using the Hilbert–Huang Transform

Vertical Distribution of Particulates within the Near-Surface Layer of Dry Bulk Port and Influence Mechanism: A Case Study in China

Contact Info

Product

Resources

About