- Compact style
- Indico style
- Indico style - inline minutes
- Indico style - numbered
- Indico style - numbered + minutes
- Indico Weeks View
Poor air quality is a concern in African cities, but governments have been too slow to react, one of the reasons being the scarcity of data on different pollutants. Instruments based on low-cost sensors and Internet of Things (IoT) are being considered as solution to evaluate the concentration of different pollutants but very few research has been done till now in Africa on this topic. Therefore, this seminar which is organized in the framework of the IoT4AQ project aims to bring together air pollution specialists who will discuss various aspects of air pollution: outdoor/indoor air pollution, air quality index, health effects, design, testing and deployment of IoT-based air quality monitors. At the end of the seminar, participants who wish to design IoT-based monitors will be invited for hands-on-training sessions.
We invite researchers, teachers and students who wish to give a talk or present their work on one of the topics to submit an abstract of around 200 words.
The participation to this seminar is totally free of charge!
For any inquiries or questions, please contact Dr. Bertrand Tchanche (bertrand.tchanche[@]uadb.edu.sn).
Transport sector, which is made up of sub-sectors, that include roadways, railways, seaways, and airways contributes to ambient air pollution and poor air quality. This work focuses solely on road traffic-related air pollution in Africa. It reviews main pollutants and sources, and their impacts on health and the environment. It explores key driving factors, such as diverse transportation modes, traffic congestion, unpaved roads, vehicle fleet conditions, urban planning challenges, fuel quality disparities, road design issues, and traffic management. Health outcomes associated with long-term exposure to road traffic pollutants are numerous, ranging from headaches, pulmonary and respiratory outcomes and cardiovascular issues to developmental and cognitive issues and increased mortality associated burden. Few recommendations and solutions are formulated, and include public sensitization, deployment of monitors, adequate regulation, and policy. A comprehensive approach is needed, and few solutions or proposals to be investigated and adapted to specific contexts are more investment to improve public transportation, development of green infrastructure and modes integration, sustainable urban planning, and stringent vehicle emission standards. Even though some of these solutions could be difficult to implement in the African countries, they are necessary if we want to reduce mortality due to road traffic-related pollution and improve the life expectancy, especially in sub-Sahara Africa.
Keywords: roadways, transport, air pollutants, Africa, health.
Besides homes, schools are primary environments for children.
The aims are fourfold: firstly, to assess the impact of indoor environmental quality IEQ on elementary school students by evaluating ventilation rates and thermal comfort, as well as the role of Heating, Ventilation, and Air Conditioning (HVAC) systems. Secondly, it aims to investigate school building practices related to cleaning, hygiene, and materials. Thirdly, to explore the associations between IEQ in schools and students' health outcomes. Lastly, it aims to analyze the link between IEQ and students' academic performance.
The studies to be presented sampled schools from various regions: all elementary schools in Finland, 70 in the US, 5 in Nigeria, and 2 in Cardiff. The Finland study collected data through questionnaires, IEQ measurements, and standardized tests. In the US, IEQ parameters were measured alongside health and academic data from school districts. Nigerian and UK studies also measured IEQ parameters in classrooms.
The general conclusions drawn from the studies suggest that inadequate ventilation is associated with indoor temperature in temperate climates but not in tropical climates. Cleaning practices need improvement, and inadequate ventilation and temperature are linked to students’ health outcomes. Maintaining recommended temperature levels and adequate ventilation can enhance students' learning achievements.
The work assesses how vehicular traffic pollution affects the cardiovascular and respiratory health of people living in Tema Community One. It links environmental and transportation issues to air quality and shows how contact with polluted air negatively affects health and social outcomes. Objectives focus on measuring the quality of the air, the spatial variation of pollution from selected gases, environmental factors promoting vehicular traffic pollution, perceptions of vehicular traffic pollution, and health effects on the cardiovascular and respiratory systems. In order to achieve this, medical doctors and household members in the study area were interviewed and engaged. Hospital records were collected from the various health facilities visited. Charts, tables, models, maps, and transcriptions are used in explaining and showing results. The work starts in chapters one to five with five objectives and five research questions. An exploratory and mixed research approach was used for the study. Questionnaires, an interactive guide, and gas sensors were used for the data collection.
Introduction: A rising worry is the deterioration of outdoor air quality, especially in urban residential areas. The main topic of this work is the measurement of significant air pollutants outdoor at Ikere-Ekiti, Nigeria, such as CO2, NO2, O3, PM1, PM2.5, and PM10, as well as their correlation with the Air Quality Index (AQI). Research Question: This study's main research question is: What can be learned through continuous monitoring with an inexpensive sensor regarding how different CO2, NO2, O3, PM1, PM2.5, and PM10 concentrations affect the computed Air Quality Index (AQI) in a residential area? Methodology: In an apartment building in Ikere-Ekiti, a month-long surveillance monitoring was carried out. Pollutant concentrations were measured using a low-cost sensor, and the AQI values were calculated using the information obtained. Results and Discussion: High levels of CO2 (582.74 ppm), NO2 (177.68 ppb), O3 (68.01 ppb), PM1 (9.28 µg/m³), PM2.5 (13.17 µg/m³), and PM10 (14 µg/m³) were found in the results. The AQI values highlighted the potential health concerns linked to the detected pollution levels by indicating air quality ranging from moderate to unhealthy. Conclusions: The findings offer insightful information about the dynamics of air pollution and its effects on a household setting. The study highlights the value of inexpensive sensors for monitoring the quality of the air in real time and highlights the necessity of focused interventions to reduce pollution and enhance the well-being of locals in cities like Ikere-Ekiti.
Keywords: Air Pollution, Air Quality Index (AQI), Case Study, Comprehensive Assessment, Ikere-Ekiti, Residential Areas.
In developing countries, recycling of electrical and electronic waste (e-waste) has attracted much attention as a significant source of flame-retardants. In this study, ten air samples were collected in 2022 from five different locations to include an electronic waste dumpsite and control site in Lagos, Nigeria; to investigate the occurrence of a range of 7 congeners of Organophosphate Flame Retardants (OPFRs) which include tris(2-chloroethyl) phosphate (TCEP), tris(2- chloroisopropyl) phosphate (TCIPP), tris(1,3-dichloro-2-propyl) phosphate (TDCIPP), amongst others.
The highest mean concentration of OPFRs was found in the indoor repair and storage shop (12,770 pg/m3); followed by the indoor dismantling shop (10,505 pg/m3). TCIPP had the highest mean concentration for all samples (15230 pg/m3), followed by TCEP (15,040 pg/m3) while the least was EHDPP (257 pg/m3). Although all target compounds were detected in both target and control sites, but the concentrations from outdoor samples were comparatively lower than the indoor air samples; and concentrations from the control sites were lower compared to target sites. This suggests that accumulation of electronic wastes contributes immensely to the concentration as well as exposure to OPFRs.
This study reports for the first time occurrence of OPFRs in atmospheric samples from e- waste dumpimg site in Lagos, Nigeria.
Processes controlling pollutant dispersion over complex terrain are much more complicated than over flat areas, as they are affected by atmospheric interactions with the orography at different spatial scales. In particular, thermally-driven daily-periodic winds produce circulation patterns and stability situations which are quite difficult to reproduce by numerical weather prediction models in their operational settings. The basics of these processes will be briefly reviewed, along with open questions and challenges to our capability for better understanding and representing atmospheric processes controlling the fate of pollutants over mountainous areas, as well as possible solutions.
Already the name IoT tells us, that there are two basic components:
This lecture describes the things part of IoT
Interfacing sensors and actuators
After a short introduction of the basic building blocks an IoT system is composed of, sensor readout and control is described. A micro-controller acting as IoT gateway must provide the necessary device interfaces as well as a network (WiFi) interface. A few of these device interfaces are shown. As an example, reading of an I2C based air quality sensor and a dust sensor with a serial interface are demonstrated. The simplest way of programming the micro-controller, is the use of either the Arduino SDK, where programs are implemented in the C++ programming language or using MicroPython, a Python-3 interpreter that can be installed on the micro-controller. The demonstration will be done with MicroPython.
Once the sensor data are available they should be transferred onto the Internet. Again there are several possibilities to accomplish this. First, the micro-controller must get access to the network, which we do by connecting it through its WiFi station interface. Then we can access it through the TCP protocol. It is possible to install a WEB server with dynamic WEB pages onto the micro-controller, that can give access to the sensor data. The WEB server uses the HTTP protocol running on top of TCP for its communication. Alternatively MQTT is a light weight protocol, which is often used in micro-controller applications to communicate between the micro-controller and a bigger machine.
Last not least IoT platforms are available, providing dash boards with graphical user interface elements like gauges, graphs, buttons and sliders which can be used to collect data from the micro-controller, communicating through HTTP or MQTT or to control devices, attached to it.
Introduction and objectives: Air pollution is the contamination of the indoor or outdoor environment by any chemical, physical or biological agent that alters the natural characteristics of the atmosphere. Indeed, the concentration of the main pollutants has increased due to the scale of fossil fuel-consuming and polluting activities. Among these pollutants, fine particles (PM) represent a major indicator of air quality. The objective of this work is to evaluate the evolution of pollution by fine particles inside an ordinary apartment in Algeria and more precisely in its kitchen. Methods: The level of pollution by fine particles was measured for 8 days, in January 2023, using the APOMOS system (Air Pollution Monitoring System) which includes a multi-sensor card of the ZPHS01B-Winsen type. It is equipped with the PMS7003M sensor dedicated to the detection of fine particles. Results: The study shows that in the study area, particle pollution inside the house reaches levels which sometimes exceed the limit value which is 80 µg/m3 and reach alarming rates. Conclusion: this study confirms that particle pollution inside the house is significant when gas cooking is in operation.
KEY WORDS: Fine particles; Indoor pollution; Optical sensor, Gas cooking
Numerous quarry operations in Southeastern Nigeria offer employment opportunities for the locals and generate income for the government. These businesses do, however, frequently cause air pollution, and the deadliest is PM2.5 which has been found to have a deleterious impact on humans and the ecosystem. The Extech Model VPC300 sensor was used to measure PM2.5, PM10, and some meteorological factors at the four quarry sites and their surroundings. The quarry locations and their surroundings were found to have high concentrations of particulate matter that exceeded the international standard. Using the methodology for assessing human health risks, estimates of the potential health risks associated with exposure to particulate matter were made. Nearly all of the quarry's surroundings had a Hazard Quotient > 1 for PM2.5 for infants, children, and adults on an acute or chronic basis. A kilometer distant from the quarry sites, PM2.5, and PM10 show the strongest association matrix with the highest value of 0.9358. Additionally, at the quarry, there is a strong relationship between the PM2.5 and temperature at 0.7860. Based on the findings, it is strongly advised that a dust control system be established.
Air Pollution and Health Impacts
The onset of Covid-19 lock-downs support the fact that man-made activities greatly contribute to air pollution (Hammer et al. 2021). Exposure to major air pollutants such as particulate matter, ground-level ozone, NO2, SO2, CO, etc, is estimated to cause millions of deaths annually (Neira and Prüss-Ustün 2016; WHO 2023), and the burden is more pronounced in low- and middle-income countries. A few studies have been conducted on the quality of air in Uganda (Kirenga et al. 2015; Onyango et al. 2019), mostly concentrated on urban areas and measurements done in a short period of time due to limited resources. Our project aims at comparing some of these in situ measurements with satellite-derived measurements and identify possible trends in the data. This will enable us model and make predictions as well as engage policy-makers, create public awareness, based on our research findings.
References:
Hammer, Melanie S. et al. 2021. “Effects of COVID-19 Lockdowns on Fine Particulate Matter Concentrations.” Science Advances 7(26): 1–11.
Kirenga, Bruce J. et al. 2015. “The State of Ambient Air Quality in Two Ugandan Cities: A Pilot Cross-Sectional Spatial Assessment.” IJER&PH 12(7)
Neira, M., and A. Prüss-Ustün. 2016. “Preventing Disease through Healthy Environments: A Global Assessment of the Environmental Burden of Disease.” Toxicology Letters 259: S1.
Onyango, Silver, Beth Parks, Simon Anguma, and Qingyu Meng. 2019. “Spatio-Temporal Variation in the Concentration of Inhalable Particulate Matter (PM10) in Uganda.” IJER&PH 16(10).
WHO. 2023. World Health Statistics 2023: Monitoring Health for the SDGs.
In the framework of the IoT4AQ project, investigations are being carried out to develop and popularize inexpensive and climate adapted monitors. Local parameters including dust concentration levels, weather data, communications systems, and electrical grids have been analysed. IoT components including LCD screens, LEDs, RTC modules, GPRS modules, microcontrollers (Arduino, Raspberry Pi, ESP32, etc.) and various sensors (dust, gas, temperature, relative humidity, etc.) available on market have been assessed and used. The basic design tested integrates a ESP32/Wi-Fi module and a SDS011 nova PM sensor. This configuration has been tested in the laboratory to map its performance. The PM and the temperature/humidity sensors performed well.
In the framework of the IoT4AQ project, investigations are being carried out to develop inexpensive and climate adapted air quality monitor. Local parameters including dust concentration levels, weather data, mobile communications systems, and electrical grids have been analysed. Geolocation, data storage and autonomous operation options were studied. IoT components including LCD screens, LEDs, RTC modules, GPRS modules, microcontrollers (Arduino, Raspberry Pi, ESP32, etc.) and various sensors (dust, gas, temperature, relative humidity, etc.) available on market have been assessed and used. The basic design proposed here integrates an Arduino Uno 328 micro-CPU, a Sim card SIM900A and a dust sensor, the PPD42NS. It features low power consumption, low cost (~100 €), light weight (~200 g), integrates a battery for autonomous operation and can accommodate more settings. It has been tested and calibrated using a reference device.
The quality of life and human activities are significantly impacted by several elements, one of which is the weather and climatic patterns. To measure the weather parameters requires weather stations which are expensive. Hence, there is a need to develop low-cost weather stations to make comprehensive meteorological data monitoring easy to attain across the world. The aim of this project was to design a low-cost weather station to monitor temperature and relative humidity. The weather station was designed to record data in real-time, store the acquired data in an SD card and also to display the data on an LCD. The study used an Arduino Uno board connected to a temperature and humidity sensor, a real-time clock (RTC), a micro-SD card module, and an LCD. The device was also capable of text file storage. The Arduino IDE was used to create a code that was transmitted to the Arduino microcontroller to operate the circuit. The performance of the weather station was tested by mounting it beside a standard weather station to measure the temperature and relative humidity. The data obtained were compared to the standard weather station data for calibrations. Following calibration, this low-cost weather station can be deployed in rural areas to measure weather parameters, augmenting the existing weather stations for more accurate climate predictions.
Air pollution, notably the presence of particulate matter, poses substantial hazards to both human health and the natural environment. The current particulate matter monitoring systems suffer from a practical limitation that they can measure particulate matter concentrations at only a single spatial point. This limitation constrains the ability to comprehensively understand the dynamics of particulate matter distribution. This study developed an internet of things-enabled sensor system to infer spatial particulate matter concentrations. Data collection nodes measured particulate matter concentrations across three scales (PM10, PM2.5 and PM1.0), the three weather parameters (wind speed, ambient temperature, and humidity), and spatial information (latitude and longitude), which were logged into a cloud-based server. Various machine learning models, such as Long Short-Term Memory, Artificial Neural Networks, Support Vector Regression, and RandomForest were trained using these datasets to predict spatial particulate matter distribution. The study found that Artificial Neural Networks exhibited superior accuracy based on Mean Absolute Error values, with high R^2 scores (>0.99) and low Root Mean Square Error values. However, beyond 100 meters from the reference node, prediction accuracy declined (<75%), highlighting the importance of spatial proximity. The findings of the study provides insights into a new approach for designing particulate matter sensors with capabilities extending beyond the limitations of the current single-point measurement approach. Moreover, the research shed light on the significance of including different weather parameters in the training process of machine learning models for predicting the spatial distribution of particulate matter within a specified radius.
Informed knowledge on the distribution of dust particles and humidity variations across certain geographical location offers innumerable benefits to humanity. Such information could guide astronomers to make informed decision when locating or carrying out their optical observation; Medical professionals can monitor and predict trends in lung and cardiovascular related diseases, and other host of professions that might find such information invaluable. The work presents the indigenous development of network of Air Quality data loggers that monitors the distribution of PM based on wide range of diameter sizes in microns across a certain geographical distance within the country. The network comprised of a number of standalone sub-stations each of which is a microcontroller-based instrument that incorporates PMS5003 and DHT digital sensors for obtaining meteorological parameter, SIM module for cloud-based data storage and other peripherals for optimal performance. The network features a centralized cloud-based data repository from each of the respective locations of these sub-stations. These data can be used for onward scientific analysis and meteorological prediction.
The issue of air quality in Africa has emerged as a critical public health concern. Initial assessments conducted in major African urban centers reveal that fine particulate concentrations exceed the thresholds recommended by the World Health Organization (WHO). We developed two (2) mobile and fixed devices utilizing the Internet of Things (IoT) to detect areas with high PM2.5 particle pollution, along with other indicators. The primary innovation of this study lies in the utilization of a microcontroller (ESP8266 NodeMCU) enabling not only data collection from sensors but also transmission to a server utilizing its Wi-Fi connectivity and the HTTP protocol. The device was mounted on a vehicle for one month as part of an intensive measurement campaign across the Dakar region. The outcomes facilitated the identification of hotspots in Dakar, pinpointing the city's major anthropogenic sources of particle pollution. This mobile device will play a pivotal role in the future identification of suitable areas for the installation of fixed pollution sensors.
Single and mixed nano-crystalline semiconductor oxides were obtained through wet-chemistry synthesis in form of powders. They were used as functional materials to produce metal oxide (MOX) thick film gas sensors to be used in air pollutants monitoring (i.e. carbon monoxide, nitrogen oxides, ozone and the total benzene, toluene, ethylbenzene, and xylene). Portable monitoring units based on these sensors were fabricated, including electronics for acquisition, processing and wireless data transmission. Long term trials in the field were carried out placing the sensor units near to the conventional fixed-site monitoring stations. The comparison between the temporal evolution of the conductivity changes of the sensors with the pollutants concentrations and those measured by the analytical instruments shows a good agreement for each sensor.
This presentation will cover the basics of the use of satellite remote sensing for air quality applications, including advantages and limitations of satellite data with respect to other data sources, and discuss some opportunities for satellite data to be used together with and as a supplement for in-situ measurements with IoT-based Air Sensors.
In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, mobility services, sustainability, alerting and data heatmaps for Dashboards in control room. Therefore, the challenge consists of providing high density data and services starting from scattered sensors data including traffic and air quality data. In this contribution we describe the Air Quality Monitoring System scenario adopting the Snap4City Solution involving traffic flow reconstruction model and the related traffic emissions diffusion/estimation. In particular, the mentioned solution for the vehicular traffic reconstruction is based on a mathematical model for fluid dynamics on networks via partial differential equations (PDE) that allows to detect macroscopic phenomena as traffic jams and propagation of waves backwards along roads. By means of the traffic model output is also possible to understand the impact of traffic emissions on the atmospheric pollution by means of diffusion model and some advanced technics in short and mid-long terms previsions.
There is a severe lack of air pollution data around the world. Low cost sensors (LCS) for measuring air pollution offer a possible path forward to remedy the lack of data, though they require careful calibration as the manufacturer-reported data often show large biases against reference monitors. Traditionally, calibration has occurred by co-locating LCS with local reference monitors and developing correction models using statistical techniques. To address this, we first present a series of traditional “colocation” corrections models and performance evaluations in a variety of global cities, including New York City, Kinshasa (DRC), Kampala (Uganda), Accra (Ghana), and New Delhi (India). We then present a globally-applicable Gaussian Mixture Regression (GMR) probabilistic model trained on co-locations from at least 5 global cities that span across wide temperature, relative humidity, and PM2.5 ranges. The model is tested on at least 20 independent co-location datasets that the GMR has not seen. We also compare the data corrected by the universal GMR to a more traditional, local correction factor, where available. GMR has proven successful for correcting LCS data: in Kinshasa, the GMR-corrected PurpleAir data resulted in R2 = 0.88 when compared to the MetOne BAM-1020, and in Accra, the GMR lowered the Mean Absolute Error of Clarity data from 7.51 𝜇g/m3 to 1.93 𝜇g/m3. The wide breadth of the universal GMR allows for correction of LCS data without the need for a local co-location which enables the correction of data from 10,000+ PurpleAir sensors around the world.
A brief overview of Clarity Movement's Sensing-as-a-Service air monitoring and IoT-based sensing and exploration of how the convergence of cost-effective distributed air monitoring technologies and multi-stakeholder collaborative strategies has paved the way for significant advancements in regional air quality management — including example case studies from around the world. Learn how Air Quality Management 2.0 facilitates comprehensive cost-benefit analyses and a more actionable understanding of the health and air quality co-benefits related to energy, transportation, and climate investments and policies.
Good air quality is critical to our health and well-being. Lack of air quality monitors, diverse pollution sources and unique weather patterns make air quality monitoring a difficult throughout Africa. Afri-SET, a new air quality sensors evaluation and training facility at the Department of Physics, University of Ghana is addressing these issues. The facility is evaluating low-cost sensors (LCS) from various manufacturers worldwide and developing calibration models, providing open access data, training, and capacity building in the use of LCS and reference monitors. Afri-SET is available to the public and has the potential to significantly improve air quality throughout West Africa and beyond by collecting high-quality data to facilitate more effective decision and policy making. This presentation will highlight the need for such a facility to address air quality issues,
the potential of sensor technology, and the significance of rigorous sensor evaluation in community empowerment and policy influence.