A methodology for the design of an effective air quality monitoring network in port areas

An optimized development of urban air quality monitoring network design based on particulate matters

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500 m * 500 m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

Real-time energy consumption and air pollution emission during the transpacific crossing of a container ship

This study presents the real-time energy consumption of a container ship’s generator engine on two round-trips from the West Coast of the US to the East Asian ports and analyzes the ship’s PM₁₀, PM_2.5, NO_x, SO_x, CO, and HC emissions, shore power usage, and factors affecting energy consumption. The average total energy consumption and air emissions for the two round trips were 1.72 GWh and 42.1 tons, respectively. The transpacific crossing segment had the highest average energy consumption (2848 ± 361 kWh) and pollutant emission rate (78.9 ± 10.0 kg h⁻¹). On the other hand, the West Coast of the US had the least energy consumption due to shore power adoption. Furthermore, switching from heavy fuel oil (HFO) to ultra-low-sulfur fuel oil (ULSFO) greatly reduced the emissions of PM and SO_x by > 96% and NO_x by 17.0%. However, CO and HC increased by 16.9% and 36.1%, respectively, implying incomplete combustion. In addition, the energy consumption was influenced by the number of reefers and wind. Therefore, this study recommends further research on energy-efficient reefers, generator engine optimization, and shore power adoption to reduce emissions from container ships.

Applying and Comparing LSTM and ARIMA to Predict CO Levels for a Time-Series Measurements in a Port Area

Air pollution is a major problem in the everyday life of citizens, especially air pollution in the transport domain. Ships play a significant role in coastal air pollution, in conjunction with transport mobility in the broader area of ports. As such, ports should be monitored in order to assess air pollution levels and act accordingly. In this paper, we obtain CO values from environmental sensors that were installed in the broader area of the port of Igoumenitsa in Greece. Initially, we analysed the CO values and we have identified some extreme values in the dataset that showed a potential event. Thereafter, we separated the dataset into 6-h intervals and showed that we have an extremely high rise in certain hours. We transformed the dataset to a moving average dataset, with the objective being the reduction of the extremely high values. We utilised a machine-learning algorithm, namely the univariate long short-term memory (LSTM) algorithm to provide the predicted outcome of the time series from the port that has been collected. We performed experiments by using 100, 1000, and 7000 batches of data. We provided results on the model loss and the root-mean-square error as well as the mean absolute error. We showed that with the case with batch number equals to 7000, the LSTM we achieved a good prediction outcome. The proposed method was compared with the ARIMA model and the comparison results prove the merit of the approach.

Review of Top-Down Method to Determine Atmospheric Emissions in Port. Case of Study: Port of Veracruz, Mexico

Indicators of environmental policies in force in Mexico, fossil fuels will continue to be used in industrial sectors, especially marine fuels, such as marine diesel oil, in port systems for some time. Considering this, we have evaluated several methods corresponding to a top-down system for determining fuel consumption and sulfur dioxide atmospheric emissions for the port of Veracruz in 2020 by type of ship on a daily resolution, considering a sulfur content of 0.5% mass by mass in marine fuel. After analyzing seven methods for determining sulfur dioxide atmospheric emission levels, Goldsworthy’s method was found to be the best option to characterize this port. The port system has two maritime zones, one of which is in expansion, which represented 55.66% of fuel consumption and 23.05% of atmospheric emissions according to the typology of vessels. We found that higher fuel consumption corresponded to container vessels, and tanker vessels represented higher atmospheric emission levels in the berthing position. The main differences that we found in the analysis of the seven methods of the top-down system corresponded to the load factor parameter, main and auxiliary engine power, and estimation of fuel consumption by type of vessel.

Environmental and human health impacts of cruise tourism: A review

The intensive growth of cruise tourism worldwide during recent decades is leading to growing concerns over the sector's global environmental and health impacts. This review combines for the first time various sources of information to estimate the magnitude of the cruise industry's environmental and public health footprints. This research shows that cruising, despite technical advances and some surveillance programmes, remains a major source of air, water (fresh and marine) and land pollution affecting fragile habitats, areas and species, and a potential source of physical and mental human health risks. Health risks impact both the people on board (crew and passengers) and on land (workers of shipyards where cruise ships are dismantled and citizens inhabiting cities with cruise ports and shipyards). In this context, we argue that the cruise industry should be held accountable with more monitoring and regulation to prevent or minimize the growing negative environmental and human health impacts.

A proposed population-health based metric for evaluating representativeness of air quality monitoring in cities: Using Hong Kong as a demonstration

City air quality monitoring (AQM) network are typically sparsely distributed due to high operation costs. It is of the question of how well it can reflect public health risks to air pollution given the diversity and heterogeneity in pollution, and spatial variations in population density. Combing high-resolution air quality model, spatial population distribution and health risk factors, we proposed a population-health based metric for AQM representativeness. This metric was demonstrated in Hong Kong using hourly modelling data of PM10, PM2.5, NO2 and O3 in 2019 with grid cells of 45m * 48m. Individual and total hospital admission risks (%AR) of these pollutants were calculated for each cell, and compared with those calculated at 16 monitoring sites using the similarity frequency (SF) method. AQM Representativeness was evaluated by SF and a population-health based network representation index (PHNI), which is population-weighted SF over the study-domain. The representativeness varies substantially among sites as well as between population- and area-based evaluation methods, reflecting heterogeneity in pollution and population. The current AQM network reflects population health risks well for PM10 (PHNI = 0.87) and PM2.5 (PHNI = 0.82), but is less able to represent risks for NO2 (PHNI = 0.59) and O3 (PHNI = 0.78). Strong seasonal variability in PHNI was found for PM, increasing by >11% during autumn and winter compared to summer due to regional transport. NO2 is better represented in urban than rural, reflecting the heterogeneity of urban traffic pollution. Combined health risk (%ARtotal) is well represented by the current AQM network (PHNI = 1), which is more homogenous due to the dominance and anti-correlation of NO2 and O3 related %AR. The proposed PHNI metric is useful to compare the health risk representativeness of AQM for individual and multiple pollutants and can be used to compare the effectiveness of AQM across cities.

Shipping and Air Quality in Italian Port Cities: State-of-the-Art Analysis of Available Results of Estimated Impacts

Populated coastal areas are exposed to emissions from harbour-related activities (ship traffic, loading/unloading, and internal vehicular traffic), posing public health issues and environmental pressures on climate. Due to the strategic geographical position of Italy and the high number of ports along coastlines, an increasing concern about maritime emissions from Italian harbours has been made explicit in the EU and IMO (International Maritime Organization, London, UK) agenda, also supporting the inclusion in a potential Mediterranean emission control area (MedECA). This work reviews the main available outcomes concerning shipping (and harbours’) contributions to local air quality, particularly in terms of concentration of particulate matter (PM) and gaseous pollutants (mainly nitrogen and sulphur oxides), in the main Italian hubs. Maritime emissions from literature and disaggregated emission inventories are discussed. Furthermore, estimated impacts to air quality, obtained with dispersion and receptor modeling approaches, which are the most commonly applied methodologies, are discussed. Results show a certain variability that suggests the necessity of harmonization among methods and input data in order to compare results. The analysis gives a picture of the effects of this pollution source, which could be useful for implementing effective mitigation strategies at a national level.

Study of the Percentage of Greenhouse Gas Emissions from Aviation in the EU-27 Countries by Applying Multiple-Criteria Statistical Methods

The transport sector, including air transport, represents an important source of air pollution. The present article deals with the current situation regarding greenhouse gas emissions in the air in 27 European Union (EU-27) member states. Every member state is characterized by selected parameters that determine the unique nature of a particular country (e.g., population, area, life expectancy, gross domestic product (GDP) per capita, etc.). In addition to these parameters, there were also other parameters which were monitored as they characterize the amount of greenhouse gas emissions and the impact of aviation on these emissions. The main purpose of the article is to compare the European Union member states on the basis of 15 examined parameters. The identification of similarities between the EU-27 member states with regard to the selected parameters was carried out by applying principal component analysis (PCA) and hierarchical cluster analysis. The average linkage method was applied to create a dendrogram representing the similarities between the examined member states. The value of the cophenetic correlation coefficient CC = 0.923 confirmed the correct application of the average linkage method. The cluster analysis outputs were five similarity-based homogeneous groups (clusters) into which the 27 member states were divided on the basis of the examined variables.