Sage Journals: Discover world-class research

Abstract

Air quality in Africa has become a major public health issue. Initial assessments conducted in large African cities show that fine particle concentrations exceed the thresholds recommended by the World Health Organization (WHO). We have developed a mobile device using the Internet of Things (IoT) to monitor particulate pollution levels (PM10, PM2.5), as well as other indicators. The key innovation of this project lies in the integration of a microcontroller (ESP8266 nodeMCU) that not only collects the data measured by the sensors but also transmits them to a server in real-time via Wi-Fi connectivity and the HTTP protocol. This device was installed on a vehicle for 1 month as part of an intensive measurement campaign conducted in the Dakar region of Senegal. The results obtained made it possible to identify the most polluted areas of the city (hot spots) and the peak pollution hours during the day.

Keywords

Air pollution Internet of Things (IoT)fine particles ESP8266 NodeMCU HTTP

Introduction

Air pollution and climate change are among the greatest environmental threats to human health. Exposure to air pollution increases health risks, a situation of particular concern for individuals with chronic diseases, the elderly, pregnant women, and children.¹

In West Africa, air pollution has become a growing problem due to rapid and uncontrolled urbanization, the expansion of urban areas, population growth, the intensification of industrial activities, and a poorly structured road transport system.^2,3

Dakar, the capital of Senegal, stands out as one of the most populous cities in West Africa, with a population exceeding 3 million inhabitants.⁴ This high population density, exacerbated by a rapid urbanization rate, places significant pressure on urban infrastructure and natural resources. According to a UN-Habitat,⁵ the city faces major challenges in waste management, air pollution, and traffic congestion, problems that are further intensified by the constant influx of new residents. Studies reveal that the concentration of fine particulate matter (PM1.0, PM2.5, PM10) in Dakar’s air frequently exceeds the thresholds recommended by the World Health Organization (WHO), posing a serious public health risk.⁶ The combined effects of population growth and unplanned urbanization exacerbate these environmental issues, making sustainable management of the city a critical challenge for local authorities.

The Internet of Things (IoT) has become an essential tool for continuous and real-time monitoring of atmospheric pollutants in densely populated urban areas.^7,8 Through connected sensors distributed across the territory, it is now possible to collect precise data on air quality, providing a detailed view of the spatial and temporal distribution of pollutants. This innovative approach not only allows for real-time tracking of pollutant concentration levels but also helps identify high-risk areas and better understand the impact of local pollution sources. Studies show that IoT significantly enhances the ability of authorities to respond quickly in the event of pollution threshold exceedances, thereby contributing to better urban air quality management.^9,10

In this context, we have developed a mobile device using IoT technology to measure real-time concentrations of fine particulate matter (PM2.5, PM10) along with other environmental indicators such as temperature and relative humidity. This device aims to identify pollution sources and enhance the spatial assessment of particulate pollutants in Dakar. By providing detailed and localized data, it facilitates the detection of critical areas and improves the understanding of pollution dynamics within the city.^11–13 The sensors in our device collect essential data such as pollution levels, temperature, humidity, and geolocation, which are transmitted in real-time to a server via Wi-Fi using the HTTP protocol, thanks to ESP8266 NodeMCU microcontrollers.¹⁴ This device enables continuous and reliable data collection, which is stored and analyzed on the server to identify particulate pollution hotspots of anthropogenic origin in the Dakar region, Senegal. The data analysis will allow for the identification of the most critical areas, providing a detailed insight into urban pollution dynamics.

This article is structured as follows: Section 2 outlines the adopted methodology and describes the tools used. Section 3 focuses on the design of the data acquisition and transmission system as well as the implementation of the device. Finally, Section 4 presents the results of the field tests.

Methodology and tools

In this section, we describe the methodology followed and the tools used for the implementation of the device.

Methodology

The system proposed in this article consists of several functional units, as illustrated in Figure 1.

Figure 1.

Functional diagram of the system.

Each unit of the device plays a crucial role in its overall functionality. First, the detection unit, equipped with advanced sensors, measures various environmental parameters such as fine particulate concentrations, temperature, and humidity. These sensors are critical for ensuring the accuracy and reliability of the collected data.^15,16

Next, the processing unit manages and controls the operation of the sensors. It handles the initial processing of raw data, ensuring that only relevant and accurate information is transmitted for further analysis. Real-time data processing optimizes system efficiency and reduces the risk of potential errors.¹⁷

Subsequently, the communication unit, which acts as a gateway, ensures the transmission of data between the sensors and the central server. Utilizing robust and secure communication protocols, it guarantees a stable and rapid connection, which is essential for real-time monitoring of pollutants.¹⁷

Finally, the collection and analysis unit is responsible for the secure storage of the collected data and its subsequent analysis. By centralizing the information, it enables a thorough and continuous evaluation of environmental conditions, thereby facilitating the identification of trends and informed decision-making.¹⁸

Materials

The proposed device consists of a set of tools including sensors, a microcontroller, and a server, each playing a crucial role in the overall functionality of the system. The primary sensor of this device, the PM2.5 Sensor Adapter v.2, as illustrated in Figure 2, is specifically designed to measure concentrations of PM1.0, PM2.5, and PM10 particles.

Figure 2.

PM2.5 pollution sensor adapter V2.0.

Additionally, this sensor incorporates a unique chip capable of measuring other environmental parameters. This versatile monitor can be integrated into various instruments dedicated to environmental condition monitoring.¹⁹

Figure 3 then shows the DHT22 sensor, used for measuring temperature and relative humidity. This sensor communicates with a microcontroller via a serial port. Factory-calibrated, it is ready to use and requires no additional components for its operation.²⁰

Figure 3.

Temperature and humidity sensor DHT22.

Figure 4 shows the GT-U7 GPS module used for retrieving geographic coordinates. This standalone GPS receiver, equipped with the high-performance u-blox 6 positioning engine, is both compact and cost-effective, with dimensions of only 16 mm × 12.2 × 2.4 mm. Its reduced size and flexible power and connectivity options make it ideal for battery-operated mobile devices. The GT-U7 offers a time-to-first-fix (TTFF) of less than one second and enables rapid satellite acquisition, making it a high-performance choice for applications requiring precise geolocation.²¹

Figure 4.

Goouuu tech GT-U7 GPS sensor.

Finally, the microcontroller used for our mobile device is the ESP8266 NodeMCU, as illustrated in Figure 5. This microcontroller integrates a Wi-Fi module, making it very user-friendly. Lightweight and with superior memory and processing capabilities compared to Arduino, it allows for the creation of a server capable of hosting a web page, thus facilitating remote control of the microcontroller. This web page can display the values measured by the NodeMCU or control the microcontroller’s I/O operations. Additionally, it can operate autonomously or function as a UART-to-Wi-Fi adapter, enabling other microcontrollers to connect to a Wi-Fi network.^22,23

Figure 5.

Microcontroller ESP8266 NodeMCU 12E.

Design and implementation

Design

Figure 6 illustrates the overall architecture of the air quality measurement station. The detection system relies on sensors capable of measuring various geophysical parameters. The collected data are transmitted to the server via the Internet using the integrated Wi-Fi module in the ESP8266 NodeMCU, employed for the mobile station. Once on the server, this data is processed, displayed on a web platform, and then overlaid on a map for precise geographical visualization.²⁴

Figure 6.

Overall architecture of the air quality monitoring station.

The air quality monitoring system we have developed relies on a network of low-cost sensors capable of measuring pollutant concentrations such as PM2.5 and PM10, temperature, humidity, and the geolocation of measurement points,²⁵ as illustrated in Figure 7.

Figure 7.

Devise acquisition system.

The system is structured into three functional units, as illustrated in the previous figure. First, the detection unit consists of sensors specifically designed to measure physical parameters such as fine particulate concentrations (PM2.5 and PM10), air humidity, and ambient temperature. Among the sensors used are the PM2.5 Sensor Adapter for detecting fine particles and the DHT22 for measuring humidity.²⁶ These raw data are then augmented by the localization unit, which provides real-time geolocation of the device using a GPS sensor. This step is crucial for accurately mapping the spatial distribution of pollutants.²⁷ Finally, the collected data are immediately accessible through the display unit, which uses an LCD screen to visualize real-time measurements. This feature is particularly useful for the mobile device, offering direct access to critical information, such as pollution levels, without requiring a connection to another device or platform for analysis.

The signals captured by these different units are then processed by the ESP8266 NodeMCU microcontroller. This component plays a central role not only in interpreting and analyzing the data but also in decision-making for data transmission.²⁸ With its low energy consumption and native Wi-Fi integration, this microcontroller is perfectly suited for IoT (Internet of Things) applications, where energy efficiency and wireless connectivity are essential for maintaining continuous and reliable environmental monitoring.

Once processed, the data must be transmitted in real-time for effective monitoring. This is where the transmission system comes in, as shown in Figure 8, which combines hardware and software components to ensure robust connectivity between different networks. Using the Internet connection, data are sent directly to a server, thus ensuring real-time updates and immediate accessibility of the information.²⁹ The ESP8266 NodeMCU microcontroller, with its integrated Wi-Fi module, facilitates this transmission via the HTTP protocol, providing stable connectivity and simplified integration with web servers and data processing platforms.³⁰ This technical setup is essential for the smooth and continuous management of collected information.

Figure 8.

Transmission system.

The final stage of the process is the storage and monitoring system, which plays a crucial role in the long-term management of environmental data. By connecting to the server, this system feeds into a dedicated web platform where measurements can be viewed in real-time through interactive graphs and maps. Users can review this data over various periods, whether a day, a month, a year, or any other specified timeframe. Additionally, the platform offers data export functionalities in various formats, facilitating further analysis or research.³¹ Thus, each unit of the system, from detection to monitoring, works in synergy to provide a comprehensive and effective solution for air quality tracking.

Implementation

The diagram plays a crucial role in visualizing and understanding the data flow within the environmental monitoring system. It clearly and systematically illustrates the successive stages of data collection, processing, transmission, and visualization, as shown in Figure 9.

Figure 9.

Operating diagram of the mobile device.

The system begins with the activation of integrated sensors, specifically designed to measure critical environmental parameters such as fine particulate concentrations (PM2.5 and PM10), geographic location via GPS, as well as air humidity and temperature. These sensors collect data every 5 s, a frequency chosen to ensure continuous and accurate monitoring, which is essential during measurement campaigns where rapid variations in atmospheric conditions and pollution levels need to be captured in real time.

Once the data is collected, it is immediately sent to the microcontroller. This central component processes and analyzes the information, ensuring coordination between the different units of the system.

There are two options for utilizing the data. On one hand, it can be displayed instantly on an integrated LCD screen, providing real-time visualization of environmental measurements. This feature is particularly valuable for mobile users, allowing them to access information directly without needing to connect to another platform.

On the other hand, the data is also transmitted via Wi-Fi to a remote server. This server stores all collected information, enabling detailed analysis and access at any time. An intuitive web interface allows for access to this data, presented in the form of interactive graphs and maps. This interface facilitates the visualization of air quality variations over defined periods, offering a valuable tool for real-time monitoring and informed decision-making based on accurate data. By following this process, the system ensures rigorous data collection, efficient processing, reliable transmission, and clear visualization of environmental data, contributing to better management and understanding of air quality. Collecting data every 5 s is a strategic choice, particularly during measurement campaigns, as it allows for capturing rapid variations and providing a detailed view of environmental conditions over short periods.

After the design of the device, a measurement campaign was conducted in the Dakar region to analyze variations in fine particulate concentrations (PM) at various locations. The device was deployed at several strategic sites, allowing for a comprehensive assessment of pollution levels across the region.

Figures 10 and 11 illustrate the installation of the mobile station on the roof of a car, facilitating data collection in various urban environments. This mobile deployment method provides extensive coverage and allows for representative data collection, offering the necessary flexibility to measure pollution in diverse and dynamic areas. By integrating this mobile approach, we achieve a complete and accurate view of atmospheric pollution in Dakar, which is crucial for an effective assessment of sources and levels of contaminants.³²

Figure 10.

The mobile device in its case.

Figure 11.

Deployment of the device on the roof of the car.

Results and discussion

To assess pollution levels using the developed device, an intensive measurement campaign was conducted over a period of 1 month. The device was mounted on a vehicle that traveled daily from morning to evening across the Dakar region. This approach allowed for the collection of data on fine particulate matter (PM) concentrations at various times of the day and in different locations within the Dakar region. To ensure the accuracy and quality of the collected data, environmental parameters, such as temperature and relative humidity, were monitored throughout the particle detection process. During the measurement period, the average ambient temperature was 28°C, with a relative humidity of approximately 68%. To ensure the reliability of the results, a regularly calibrated Purple Air sensor was used alongside our own device. Strict data collection and analysis protocols were implemented, including repeated measurements to ensure reproducibility. Data validation was performed through iterative testing and comparison with reference measurements provided by Purple Air sensors.³³

By mapping pollution levels and identifying the main sources of particulate pollutants, this campaign provided essential information for understanding pollution dynamics in the region and for guiding decision-making.

Figure 12 depicts the 30 distinct routes covered by the vehicle to ensure optimal coverage of the region. These routes were selected to pass through areas with significant and varied pollution sources. The total distance covered by these 30 routes is 2668 km. Each day, the vehicle was required to follow the predefined route between 9:00 AM and 7:00 PM.

Figure 12.

Deployment of the device on the roof of the car.

To illustrate the variation in fine particulate (PM) concentrations during peak hours throughout the day, the route from Avenue Cheikh Anta Diop to downtown was selected for this study. The maps in Figures 13 and 14 show the mapping of PM levels (PM10 and PM2.5) along this route. Particularly high concentrations are observed in downtown (with PM2.5 levels > 90 $μ$ g/m³), especially between 12:00 and 17:00. PM10 concentrations are also notably high in these source areas during peak hours.

Figure 13.

Variation in PM2.5 concentrations ( $μ$ g/m³) between the University and downtown Dakar.

Figure 14.

Variation in PM10 concentrations ( $μ$ g/m³) between the University and downtown Dakar.

Figure 15 shows the daily cycle of PM10 pollution levels along the study route. High concentrations of 80–140 $μ$ g/m³ are observed during peak traffic hours, morning (10–11 a.m.) and late afternoon (4–5 p.m.). In contrast, concentrations measured during the middle of the day, between 12 p.m. and 4 p.m., are generally lower, often below 80 $μ$ g/m³.

Figure 15.

Diurnal variation of PM10 concentrations from 10 a.m. to 5 p.m.

Indeed, PM10 and PM2.5 levels increase in these areas due to several key factors. Intense road traffic in the city center, characterized by high vehicle density, generates exhaust emissions and fine particulate matter, exacerbated by prolonged traffic congestion.³⁴ Additionally, industrial activities contribute to air pollution, especially during peak hours when economic activity is at its highest.³⁵ This is compounded by natural particulate pollution from the desert, which maintains a consistently high background level.³⁶

Several studies indicate that pollutant emissions from ships in port areas are particularly high in terms of PM10 and PM2.5.^37,38 The specific emissions (SE) from container ships, passenger vessels, and oil tankers significantly contribute to pollution. Container ships, in particular, emit considerably more pollutants than other types of vessels. Furthermore, the spatiotemporal distribution of these emissions can vary significantly depending on the type of ship and navigation conditions, which may also explain the high concentrations observed near the Port of Dakar.

Moreover, weather conditions, with light winds during these periods, limit pollutant dispersion and exacerbate pollution.³⁹

To identify the sources and types of particles, the PM2.5/PM10 ratio is calculated.⁴⁰ This indicator is widely used to trace the origin of pollution: a high ratio (close to 1) typically indicates an anthropogenic source, usually linked to industrial activity or traffic, while a lower ratio is associated with natural pollution, such as desert dust particles.⁴¹ Figure 16 illustrates this parameter, highlighting the sources and types of fine particles in the Dakar region. Four main pollution hotspots are identified based on particle sources and types. Hotspots 1 and 2 have ratios close to 1, suggesting anthropogenic pollution likely tied to transportation and other human activities such as industry and waste burning. Hotspot 3, with a ratio below 0.5, indicates the presence of dust emissions from cement factories located around Rufisque. Lastly, Zone 4, with a ratio around 0.5, corresponds to an uninhabited area where natural particles are observed, suggesting that pollution in this region may originate from natural sources, primarily desert particles carried by the wind.

Figure 16.

PM2.5/PM10 ratio for identifying sources and types of particulate pollution in the Dakar region.

Thus, these results highlight the urgency of implementing pollution management strategies with appropriate decision-making to reduce pollution levels. They also underscore the need for continuous measures to mitigate impacts on public health.

Conclusion

This article presents a low-cost air quality monitoring device based on the Internet of Things (IoT). This innovative system is distinguished by its ability to combine mobility and precision, enabling real-time tracking of fine particulate concentrations (PM2.5 and PM10) in urban environments, such as Dakar.

Unlike traditional fixed stations, our mobile device is characterized by its capacity to collect data over a wide geographical area. This provides a more comprehensive and dynamic overview of air quality variations. This approach significantly enhances spatial coverage and facilitates the identification of pollution sources.

One of the strengths of this system is the instantaneous transmission of data via a Wi-Fi connection, allowing for real-time processing. This feature is crucial for enabling rapid responses during pollution episodes and informing policy and public health decisions with up-to-date data.

Analysis of the collected data has revealed high concentrations of fine particles in certain urban areas, particularly during peak hours. It has also allowed for the identification of pollution hotspots, primarily attributed to anthropogenic activities. This information is valuable for better understanding pollution dynamics in urban settings.

Finally, this device provides researchers and authorities with an effective tool for monitoring air quality. It serves as a solid foundation for the development of targeted strategies aimed at reducing the negative impacts of pollution on public health.

Footnotes

On behalf of all authors,the corresponding author states that there is no conflict of interest.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Ahmed Gueye

Mamadou Simina Drame

Serigne Abdoul Aziz Niang

Data availability statement

The data underlying our article will be available upon request,in accordance with the publisher’s policies and subject to relevant ethical and legal considerations. To access the data,please contact ahmed.gueye@ucad.edu.sn .

References

Ghebreyesus

Pollution de l’air : les nouvelles lignes directrices de l’OMS, 2021. Directeur général de l’OMS. https://apps.who.int/iris/?locale-attribute=fr\& (accessed 14 August 2024).

Mehndiratta

Jain

Srivastava

, et al. Environmental pollution and nanotechnology. Environ Pollut 2013; 2(2): 49. https://api.semanticscholar.org/CorpusID:54723536.

Huang

Zhao

Zhou

, et al. Air quality forecast monitoring and its impact on brain health based on big data and the Internet of Things. IEEE Access 2018; 6: 78678–78688.

Ngom

Seye

Diallo

, et al. A hybrid measurement kit for real-time air quality monitoring across Senegal cities. In: 2018 1st international conference on smart cities and communities (SCCIC), 2018, pp.1–6.

ONU-Habitat. The state of African cities 2014: re-imagining sustainable urban transitions. Nairobi: United Nations Human Settlements Programme (UN-Habitat), 2014. https://unhabitat.org/state-of-african-cities-2014-re-imagining-sustainable-urban-transitions

Sow

Tchanche

Fall

, et al. Air pollution in the city of Dakar, Senegal. Meteorol 2022; 01(116): 048.

Hadji

Les fondamentaux de l’IOT, 2020. www.itu.int. ITU Union International de Telecommunication.

Nasution

Muchtar

Simon

Designing an IoT-based air quality monitoring system. IOP Conf Ser Mater Sci Eng 2019; 648(1): 012037.

Barot

Kapadia

Air quality monitoring systems using IoT: a review. In: 2020 international conference on computational performance evaluation (ComPE), 2020, pp.226–231.

10.

Ighalo

Adeniyi

Marques

Internet of Things for water quality monitoring and assessment: a comprehensive review. In: Hassanien

Bhatnagar

Darwish

(eds) Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications. Studies in Computational Intelligence, vol 912. Cham: Springer International Publishing, 2021, pp.245–259. https://doi.org/10.1007/978-3-030-51920-9_13

11.

Kortoçi

Motlagh

Zaidan

, et al. Air pollution exposure monitoring using portable low-cost air quality sensors. Smart Health 2022; 23: 100241. https://www.sciencedirect.com/science/article/pii/S235264832100057X.

12.

Gao

Yang

, et al. Experimental and simulation analysis on the effect of vibratory stress relief on impact toughness of Q345/316 L dissimilar steel welded joints. Mater Today Commun 2024; 38: 108084. https://www.sciencedirect.com/science/article/pii/S2352492824000643.

13.

Chu

Wang

, et al. Effects of ultrasonic impact treatment on the corrosion resistance of laser-cladded CrMnFeCoNi high-entropy alloy coatings. Surf Coat Technol 2024; 489: 131102. https://www.sciencedirect.com/science/article/pii/S0257897224007333.

14.

Williams

Henshaw

Bart

, et al. Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network. Meas Sci Technol 2013; 24(6): 05.

15.

Habbani

Romain

El Abbadi

, et al. Sensor networks: information acquisition and routing system. SIR Laboratory Department of Computer Engineering Mohammadia School of Engineers, 2006.

16.

Madakam

Ramaswamy

Tripathi

Internet of Things (IoT): a literature review. J Comput Commun 2015; 03(05): 164–173. https://api.semanticscholar.org/CorpusID:43291586.

17.

Kim

Jung

Data transmission and network architecture in long range low power sensor networks for IoT. Wirel Pers Commun 2017; 93(1): 119–129.

18.

Saha

Majumdar

Data centre temperature monitoring with ESP8266 based wireless sensor network and cloud based dashboard with real time alert system. In: 2017 devices for integrated circuit (DevIC), 2017, pp.307–310.

19.

DFROBOT. Air Quality Monitor (PM2.5, Formaldehyde, Temperature and Humidity Sensor), 2024. https://www.dfrobot.com/product-1612.html (accessed 16 August 2024).

20.

Jerome. Tutorial-DHT22, 2024. https://passionelectronique.fr/tutorial-dht22/ (accessed 16 August 2024).

21.

Manualsplus. Goouuu Tech GT-U7 GPS Modules, 2024. https://manuals.plus/goouuu/goouuu-tech-gt-u7-gps-modules\#google_vignette (accessed 16 August 2024).

22.

Santos

Getting Started with ESP8266 NodeMCU Development Board, 2024. Random Nerd Tutorials. https://randomnerdtutorials.com/projects-esp8266/.

23.

Patnaikuni

A comparative study of Arduino, Raspberry Pi and ESP8266 as IoT development board. Int J Adv Res Comput Sci 2017; 8(9): 2350–2352. https://api.semanticscholar.org/CorpusID:54900594.

24.

Umapathi

Sabbani

. An Internet of Things (IoT)-based approach for real-time kitchen monitoring using NodeMCU 1.0. In: Sivasubramanian

Shastry

Hong

(eds) Futuristic communication and network technologies. Singapore: Springer Nature Singapore, 2022, pp.35–43.

25.

Kim

, et al. Development of an IoT-based indoor air quality monitoring platform. J Sens 2020; 2020(1): 1–14.

26.

Setiawan

Kustiawan

IoT based air quality monitoring. IOP Conf Ser Mater Sci Eng 2018; 384(1): 012008.

27.

Hammoudeh

Epiphaniou

Belguith

, et al. A service-oriented approach for sensing in the Internet of Things: intelligent transportation systems and privacy use cases. IEEE Sens J 2021; 21(14): 15753–15761.

28.

Simitha

Subodh Raj

IoT and WSN based air quality monitoring and energy saving system in SmartCity project. In: 2019 2nd international conference on intelligent computing, instrumentation and control technologies (ICICICT), 2019, vol. 1, pp.1431–1437.

29.

Mansour

Nasser

Karim

, et al. Wireless sensor network-based air quality monitoring system. In: 2014 international conference on computing, networking and communications (ICNC), 2014, pp.545–550.

30.

Khan

Khattak

Ullah

, et al. A low-cost IoT based system for environmental monitoring. In: 2019 international conference on frontiers of information technology (FIT), 2019, pp.173–1735.

31.

Purkayastha

Mishra

Shil

, et al. IoT based design of air quality monitoring system web server for Android platform. Wirel Pers Commun 2021; 118(4): 2921–2940.

32.

Gueye

Drame

Diallo

, et al. PM2.5 hotspot identification in Dakar area: an innovative IoT and mapping-based approach for effective air quality management. In: 2023 IEEE 9th international conference on smart instrumentation, measurement and applications (ICSIMA), 2023, pp.302–307.

33.

Barkjohn

Gantt

Clements

AL.

Development and application of a United States wide correction for PM(2.5) data collected with the PurpleAir sensor. Atmos Meas Tech 2021; 4(6): 4617–4637.

34.

Rakowska

Wong

Townsend

, et al. Impact of traffic volume and composition on the air quality and pedestrian exposure in urban street canyon. Atmos Environ 2014; 98: 260–270. https://www.sciencedirect.com/science/article/pii/S1352231014006840.

35.

Duque

Relvas

Silveira

, et al. Evaluating strategies to reduce urban air pollution. Atmos Environ 2016; 127: 196–204. https://www.sciencedirect.com/science/article/pii/S135223101530621X.

36.

Drame

Jenkins

Camara

, et al. Observations and simulation of a Saharan air layer event with a midtropospheric dust layer at Dakar, Senegal, 6–7 July 2010. J Geophys Res Atmos 2011; 116(D21): DOI: 10.1029/2011jd016368.

37.

Chen

Dou

Song

, et al. Spatial-temporal ship pollution distribution exploitation and harbor environmental impact analysis via large-scale AIS data. J Mar Sci Eng 2024; 12(6): 960. https://www.mdpi.com/2077-1312/12/6/960.

38.

Xiao

Wang

, et al. Sustainable maritime transport: a review of intelligent shipping technology and green port construction applications. J Mar Sci Eng 2024; 12(10): 1728. https://www.mdpi.com/2077-1312/12/10/1728.

39.

Bernard

Samet

Grambsch

, et al. The potential impacts of climate variability and change on air pollution-related health effects in the United States. Environ Health Perspect 2001; 109(Suppl 2): 199–209. http://www.jstor.org/stable/3435010.

40.