PM2.5 is a harmful pollutant that can cause respiratory problems. New technologies are being developed to reduce PM2.5 pollution and improve air quality. Learn more about these technologies and how they are making a difference.
How New Technologies Are Revolutionizing the Way We Measure PM2.5Ambee
PM2.5 is a harmful pollutant that can cause respiratory problems. New technologies are being developed to reduce PM2.5 pollution and improve air quality. Learn more about these technologies and how they are making a difference.
EDD Project A35 group. final.pdf Department of ENTCMihirDatir1
This document describes an Arduino-based air monitoring system project. It includes an introduction describing the importance of air quality monitoring. It then lists the components of the system, which uses sensors to measure parameters like temperature, humidity, and carbon dioxide levels. The methodology section explains how the Arduino board will read analog voltage values from the sensors and use those to monitor air quality levels. It will control a filter fan if thresholds are exceeded. The system aims to continuously track air quality and display results to increase pollution awareness. It provides a low-cost solution to monitor indoor and outdoor air quality.
Benefits of Low-Cost Ambient Air Quality Monitoring SystemOizom
Low-cost Continuous Ambient Air Quality Monitoring System (CAAQMS) is compact, lightweight and cost-effective. It provides real-time high precision environment data, with geospatial and ubiquitous monitoring. Low-cost air quality sensors provide 360-degree ambient air monitoring for public awareness. The data can assist in encouraging community participation for air quality monitoring. It is the future of air monitoring in India.
Low cost ambient air quality monitoring systemOizom
The low-cost Continuous Ambient Air Quality Monitoring System (CAAQMS) is compact, scalable, cost-effective for a dense network of air quality monitors.
Read Full Article here: https://oizom.com/low-cost-air-quality-sensors-oizom/
Public health challenges and risk assessment requires more refined and varied monitoring approaches to reduce exposures and risks. Hence, authorities need to stringent pollution control measures. This can be achieved by widespread air monitoring at the microscale, which is only possible through compact, highly scalable CAAQMS.
Currently, there are no set government regulations for low-cost CAAQMS as they are still subjective to interference by ambient conditions. But looking at the huge success and rising demand for these low-cost air quality monitors, governments across the globe have started considering to include these sensors under the Air Regulations. Currently, guidelines are underway for air quality sensor technology standards in the European Union, the United States, and China. In India, The government has assigned the Council of Scientific & Industrial Research (CSIR)-National Physical Laboratory (NPL) to come up with guidelines to certify air quality monitoring instruments. CSIR-NPL will be the national verification agency for the purpose and they shall develop the necessary infrastructure, management system.
IRJET- Air Pollution Monitoring System using the Internet of ThingsIRJET Journal
This document describes an air pollution monitoring system using the Internet of Things (IoT). The system uses sensors to detect harmful gases like CO2, NOx, and smoke. It displays the air quality levels in parts per million on an LCD screen and online. When pollution levels exceed a threshold, an alarm is activated. The system measures temperature and humidity as well. Data is sent to a web server via WiFi so that air quality can be monitored remotely. The goal is to provide real-time air quality information to raise awareness and help reduce pollution by informing people about pollution levels in different areas.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IRJET- Aircop – An Air Pollution Monitoring DeviceIRJET Journal
1. The document describes a device called AIRCOP that was developed for remote monitoring of air pollution factors using Internet of Things technology.
2. AIRCOP monitors carbon monoxide, air quality, particulate matter, temperature, humidity, atmospheric pressure, and wind speed using low-cost sensors.
3. The sensor data is sent to a cloud-based IoT platform called Thingspeak where it is stored and visualized, allowing continuous monitoring of environmental conditions.
The document presents information on emerging technologies for air quality monitoring. It discusses various air pollutants like particulate matter, carbon monoxide, nitrogen oxides, and sulfur dioxides. It also describes different air sampling processes and the application of air quality index (AQI) to report daily air quality levels. The document outlines the objectives to analyze air quality data from pollution control boards and use sensors to provide cautionary values to alert people and improve air quality. It discusses literature review on indoor air quality and wireless sensor networks for air monitoring.
How New Technologies Are Revolutionizing the Way We Measure PM2.5Ambee
PM2.5 is a harmful pollutant that can cause respiratory problems. New technologies are being developed to reduce PM2.5 pollution and improve air quality. Learn more about these technologies and how they are making a difference.
EDD Project A35 group. final.pdf Department of ENTCMihirDatir1
This document describes an Arduino-based air monitoring system project. It includes an introduction describing the importance of air quality monitoring. It then lists the components of the system, which uses sensors to measure parameters like temperature, humidity, and carbon dioxide levels. The methodology section explains how the Arduino board will read analog voltage values from the sensors and use those to monitor air quality levels. It will control a filter fan if thresholds are exceeded. The system aims to continuously track air quality and display results to increase pollution awareness. It provides a low-cost solution to monitor indoor and outdoor air quality.
Benefits of Low-Cost Ambient Air Quality Monitoring SystemOizom
Low-cost Continuous Ambient Air Quality Monitoring System (CAAQMS) is compact, lightweight and cost-effective. It provides real-time high precision environment data, with geospatial and ubiquitous monitoring. Low-cost air quality sensors provide 360-degree ambient air monitoring for public awareness. The data can assist in encouraging community participation for air quality monitoring. It is the future of air monitoring in India.
Low cost ambient air quality monitoring systemOizom
The low-cost Continuous Ambient Air Quality Monitoring System (CAAQMS) is compact, scalable, cost-effective for a dense network of air quality monitors.
Read Full Article here: https://oizom.com/low-cost-air-quality-sensors-oizom/
Public health challenges and risk assessment requires more refined and varied monitoring approaches to reduce exposures and risks. Hence, authorities need to stringent pollution control measures. This can be achieved by widespread air monitoring at the microscale, which is only possible through compact, highly scalable CAAQMS.
Currently, there are no set government regulations for low-cost CAAQMS as they are still subjective to interference by ambient conditions. But looking at the huge success and rising demand for these low-cost air quality monitors, governments across the globe have started considering to include these sensors under the Air Regulations. Currently, guidelines are underway for air quality sensor technology standards in the European Union, the United States, and China. In India, The government has assigned the Council of Scientific & Industrial Research (CSIR)-National Physical Laboratory (NPL) to come up with guidelines to certify air quality monitoring instruments. CSIR-NPL will be the national verification agency for the purpose and they shall develop the necessary infrastructure, management system.
IRJET- Air Pollution Monitoring System using the Internet of ThingsIRJET Journal
This document describes an air pollution monitoring system using the Internet of Things (IoT). The system uses sensors to detect harmful gases like CO2, NOx, and smoke. It displays the air quality levels in parts per million on an LCD screen and online. When pollution levels exceed a threshold, an alarm is activated. The system measures temperature and humidity as well. Data is sent to a web server via WiFi so that air quality can be monitored remotely. The goal is to provide real-time air quality information to raise awareness and help reduce pollution by informing people about pollution levels in different areas.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IRJET- Aircop – An Air Pollution Monitoring DeviceIRJET Journal
1. The document describes a device called AIRCOP that was developed for remote monitoring of air pollution factors using Internet of Things technology.
2. AIRCOP monitors carbon monoxide, air quality, particulate matter, temperature, humidity, atmospheric pressure, and wind speed using low-cost sensors.
3. The sensor data is sent to a cloud-based IoT platform called Thingspeak where it is stored and visualized, allowing continuous monitoring of environmental conditions.
The document presents information on emerging technologies for air quality monitoring. It discusses various air pollutants like particulate matter, carbon monoxide, nitrogen oxides, and sulfur dioxides. It also describes different air sampling processes and the application of air quality index (AQI) to report daily air quality levels. The document outlines the objectives to analyze air quality data from pollution control boards and use sensors to provide cautionary values to alert people and improve air quality. It discusses literature review on indoor air quality and wireless sensor networks for air monitoring.
This document provides an abstract for a thesis submitted to GITAM University in partial fulfillment of the requirements for a Doctor of Philosophy in Computer Science. The thesis proposes developing an intelligent framework for air pollution monitoring using Internet of Things and machine learning techniques. It discusses using various sensors and an Arduino board connected to a WiFi device to monitor air quality levels. The data would be displayed on a mobile app or LCD. Various deep learning models like LSTM, RNN, GRU and CNN are explored to accurately predict air quality index and determine the optimal model. The objectives of the research are outlined as developing forecasting models using weather data and sensor density to analyze large datasets and predict pollution levels in cities.
Research on Densely-deployed Air Quality MonitoringIJAEMSJORNAL
In this paper, the limitations of the existing air quality measurement system were identified, and factors and related cases were studied to overcome them. In the existing measurement system, it is difficult to properly understand the environment in which the user lives because the area to be covered by the measurement station is too large. In addition, since the reliable measurement method is used, the average of the previous hour's data is shared, so it is not possible to quickly grasp changes in air quality that change every minute. Based on the contents examined, it is expected that the elements to be configured so that users can receive and utilize close air quality information in real time at the location where they live is expected to be concrete.
Analysis Of Air Pollutants Affecting The Air Quality Using ARIMAIRJET Journal
This document discusses analyzing air pollutants affecting air quality using the ARIMA time series model. It begins with an abstract describing the decreasing air quality due to factors like traffic and industry. It then discusses predicting and forecasting the Air Quality Index using time series models like ARIMA. The document reviews literature on previous studies analyzing air pollution data using techniques like neural networks and random forests. It describes preprocessing time series air pollution data to address missing values and assess stationarity before deploying the ARIMA model to make predictions.
Detection of Wastewater Pollution Through Natural Language Generation With a ...Shakas Technologies
Detection of Wastewater Pollution Through Natural Language Generation With a Low-Cost Sensing Platform.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
IOT ENABLED EMBEDDED BASED REAL TIME AIR QUALITY MONITORING SYSTEMMOHAMMED FURQHAN
When we breathe polluted air pollutants get into our lungs; they can enter the bloodstream and be carried to our internal organs such as the brain. This can cause severe health problems such as asthma, cardiovascular diseases and even cancer and reduces the quality and number of years of life.
Reasons behind environmental data vacuum and importance of dataOizom
Environmental data is generated through environmental monitoring. And the data scarcity is due to insufficient ambient monitoring for collecting enough data and processing it to generate useful information. When we say environmental data vacuum, one might think it may be due to one of the following reasons:
Absence of raw data
Inability to convert raw data to useful formats for decision making
Proper means to disseminate the information publicly
Is the accuracy of low-cost air quality monitoring systems a valid concernOizom
This document discusses concerns about the accuracy of low-cost air quality monitoring systems and potential solutions. It notes that while low-cost systems allow for scalable and remote monitoring, questions remain about data accuracy given sensor limitations and environmental factors. Potential solutions proposed include active sampling to avoid environmental impacts, sensor calibration, and location detection tools. The document argues that implementing these solutions can produce effective and accurate low-cost monitoring.
This document presents a framework for visualizing air quality data. It involves collecting data on various air pollutants, preprocessing the data, using a machine learning model to predict an Air Quality Index, and visualizing the results. Specifically, it trains a random forest regression model on data from the Central Pollution Control Board to predict the AQI based on parameters like PM2.5, PM10, NO2, etc. It then implements the model to make real-time predictions using an API and stores the results in a database. These predictions are visualized on an interactive web page to show users the current air quality levels. The framework aims to help people monitor local air quality and help government organizations address areas with poor air quality.
IRJET- Web-Based Air and Noise Pollution Monitoring and Alerting SyetemIRJET Journal
This document proposes an IOT-based system to monitor air and noise pollution levels in a particular region using sensors connected to a Raspberry Pi microcontroller. The sensors detect parameters like gas, humidity, and sound levels, and send the data via GSM module to the cloud for remote monitoring and analysis. The system also includes an alerting mechanism to notify users if pollution levels exceed certain thresholds.
Cambrian Engineering Corporation faced challenges with dust pollution control during a school renovation project in Singapore. Oizom's Dustroid device was installed to remotely monitor air quality in real-time by measuring various particle sizes. Dustroid's precise data enabled timely decisions on dust suppression measures to maintain optimal levels, ensuring safety and compliance. The results were accurate monitoring without site visits, empowering authorities to develop effective pollution control solutions and strategies through data-driven insights.
Design and Implementation of Portable Outdoor Air Quality Measurement System ...IJECEIAES
Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5µm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient.
AMBIENT AIR POLLUTANTYS SAMPLING AND ANALYSIS.pptxAniketChavan72
This document provides information on measuring ambient air pollutants through sampling and analysis. It discusses the objectives of ambient air monitoring to assess air quality, health impacts, and effectiveness of pollution controls. It describes the major air pollutants that are monitored, including particulate matter, SO2, NO2, CO, and others. Methods for both manual and continuous air pollution monitoring are covered. Recommended minimum numbers of monitoring stations are provided based on population. Techniques for sampling air pollutants and the factors that determine pollutant concentrations are outlined. Common equipment used includes respirable dust samplers, filter papers, gas manifolds, and UV-visible spectrophotometers. Methods of measurement for SO2, NO2
Air monitoring sensors and advanced analytics in exposure assessmentDrew Hill
https://doi.org/10.6084/m9.figshare.12354866.v2
We are in the middle of a movement in environmental sensors that is taking the world by storm— Californian governments and public health practitioners, in particular, are leading the nation in exploring and implementing environmental sensors in the production of highly granular, realtime air quality information. As this movement matures, we are seeing improved understanding of ambient exposures and insights that are truly actionable — for example informing community emissions reduction plans under the recent Assembly Bill 617. This innovation in air quality sensor science can be leveraged to improve measurements in the industrial and occupational spaces. This movement has also lead to innovations in analysis methods that facilitate exposure insights not feasible with standard filter, adsorbent, and general integrated samples. This presentation discusses recent advancements in these spaces and offer brief examples of their implementation and potential applicability toward the industrial and occupational hygiene spaces.
09.15Measuring air pollutant emissions using novel techniques.pdfIES / IAQM
This document discusses using novel techniques like remote sensing, telematics data, and sensor data to measure vehicular pollutant concentrations and emissions at high spatial and temporal resolution. Combining data streams from different devices allows the generation of detailed maps of air pollution sources, levels, and how they change over time and location. While this offers potential benefits, integrating diverse data also raises privacy and ethical concerns that need addressing.
This document discusses air pollution monitoring and different monitoring methods. It describes the need for well-planned monitoring to make rational decisions about environmental protection programs. Discrete sampling using manual methods are presented as a practical alternative for most monitoring objectives in India. High volume air samplers and respirable dust samplers are discussed as methods for ambient and workplace air quality monitoring. Continuous automatic instruments are better for surveillance but discrete sampling is more versatile and cost-effective for monitoring multiple locations and parameters.
A Smart air pollution detector using SVM ClassificationIRJET Journal
This document summarizes a research paper that proposes a smart air pollution detector using an SVM classification model. It begins with an abstract that describes the need to control rising air pollution levels in developing countries like India. It then discusses particulate matter (PM) and its health risks when concentrated. The paper proposes to regularly check PM concentration levels using machine learning techniques. It reviews related work applying models like naive Bayes, SVM and regression to predict air quality. It then describes the existing systems' limitations and proposes a system that classifies PM2.5 levels using logistic regression and forecasts levels using an SVM model for improved accuracy. The paper analyzes the results and concludes machine learning can accurately predict future pollution levels to help people be aware and take action
IRJET - Prediction of Air Pollutant Concentration using Deep LearningIRJET Journal
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An Efficient Tracking System for Air and Sound.pdfAakash Sheelvant
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Intelligent pollution monitoring using wireless sensor networks eSAT Journals
This document describes an intelligent pollution monitoring system using wireless sensor networks. The system monitors air pollution using gas sensors, water pollution using pH meters, and detects human movement using PIR sensors. When pollution levels exceed standards, it sends SMS alerts to the industry owner and pollution control board. The system aims to control pollution and protect the environment and human health. It provides continuous, real-time pollution monitoring to ensure standards are met.
Ambee's Latest Release Introducing Smoke Plumes API with Smoke Data InsightsAmbee
Explore Ambee's Latest Release: Introducing Smoke Plumes API with Valuable Smoke Data Insights. Discover how our innovative technology empowers you to monitor and mitigate the impact of smoke plumes in your area. Learn more now!
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This document provides an abstract for a thesis submitted to GITAM University in partial fulfillment of the requirements for a Doctor of Philosophy in Computer Science. The thesis proposes developing an intelligent framework for air pollution monitoring using Internet of Things and machine learning techniques. It discusses using various sensors and an Arduino board connected to a WiFi device to monitor air quality levels. The data would be displayed on a mobile app or LCD. Various deep learning models like LSTM, RNN, GRU and CNN are explored to accurately predict air quality index and determine the optimal model. The objectives of the research are outlined as developing forecasting models using weather data and sensor density to analyze large datasets and predict pollution levels in cities.
Research on Densely-deployed Air Quality MonitoringIJAEMSJORNAL
In this paper, the limitations of the existing air quality measurement system were identified, and factors and related cases were studied to overcome them. In the existing measurement system, it is difficult to properly understand the environment in which the user lives because the area to be covered by the measurement station is too large. In addition, since the reliable measurement method is used, the average of the previous hour's data is shared, so it is not possible to quickly grasp changes in air quality that change every minute. Based on the contents examined, it is expected that the elements to be configured so that users can receive and utilize close air quality information in real time at the location where they live is expected to be concrete.
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This document discusses analyzing air pollutants affecting air quality using the ARIMA time series model. It begins with an abstract describing the decreasing air quality due to factors like traffic and industry. It then discusses predicting and forecasting the Air Quality Index using time series models like ARIMA. The document reviews literature on previous studies analyzing air pollution data using techniques like neural networks and random forests. It describes preprocessing time series air pollution data to address missing values and assess stationarity before deploying the ARIMA model to make predictions.
Detection of Wastewater Pollution Through Natural Language Generation With a ...Shakas Technologies
Detection of Wastewater Pollution Through Natural Language Generation With a Low-Cost Sensing Platform.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
IOT ENABLED EMBEDDED BASED REAL TIME AIR QUALITY MONITORING SYSTEMMOHAMMED FURQHAN
When we breathe polluted air pollutants get into our lungs; they can enter the bloodstream and be carried to our internal organs such as the brain. This can cause severe health problems such as asthma, cardiovascular diseases and even cancer and reduces the quality and number of years of life.
Reasons behind environmental data vacuum and importance of dataOizom
Environmental data is generated through environmental monitoring. And the data scarcity is due to insufficient ambient monitoring for collecting enough data and processing it to generate useful information. When we say environmental data vacuum, one might think it may be due to one of the following reasons:
Absence of raw data
Inability to convert raw data to useful formats for decision making
Proper means to disseminate the information publicly
Is the accuracy of low-cost air quality monitoring systems a valid concernOizom
This document discusses concerns about the accuracy of low-cost air quality monitoring systems and potential solutions. It notes that while low-cost systems allow for scalable and remote monitoring, questions remain about data accuracy given sensor limitations and environmental factors. Potential solutions proposed include active sampling to avoid environmental impacts, sensor calibration, and location detection tools. The document argues that implementing these solutions can produce effective and accurate low-cost monitoring.
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Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5µm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient.
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This document provides information on measuring ambient air pollutants through sampling and analysis. It discusses the objectives of ambient air monitoring to assess air quality, health impacts, and effectiveness of pollution controls. It describes the major air pollutants that are monitored, including particulate matter, SO2, NO2, CO, and others. Methods for both manual and continuous air pollution monitoring are covered. Recommended minimum numbers of monitoring stations are provided based on population. Techniques for sampling air pollutants and the factors that determine pollutant concentrations are outlined. Common equipment used includes respirable dust samplers, filter papers, gas manifolds, and UV-visible spectrophotometers. Methods of measurement for SO2, NO2
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We are in the middle of a movement in environmental sensors that is taking the world by storm— Californian governments and public health practitioners, in particular, are leading the nation in exploring and implementing environmental sensors in the production of highly granular, realtime air quality information. As this movement matures, we are seeing improved understanding of ambient exposures and insights that are truly actionable — for example informing community emissions reduction plans under the recent Assembly Bill 617. This innovation in air quality sensor science can be leveraged to improve measurements in the industrial and occupational spaces. This movement has also lead to innovations in analysis methods that facilitate exposure insights not feasible with standard filter, adsorbent, and general integrated samples. This presentation discusses recent advancements in these spaces and offer brief examples of their implementation and potential applicability toward the industrial and occupational hygiene spaces.
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This document discusses air pollution monitoring and different monitoring methods. It describes the need for well-planned monitoring to make rational decisions about environmental protection programs. Discrete sampling using manual methods are presented as a practical alternative for most monitoring objectives in India. High volume air samplers and respirable dust samplers are discussed as methods for ambient and workplace air quality monitoring. Continuous automatic instruments are better for surveillance but discrete sampling is more versatile and cost-effective for monitoring multiple locations and parameters.
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As more people become aware of the importance of breathing clean air during physical activities, developers of fitness apps have a massive opportunity to make their products better for their customers. With advances in technology, these apps will be able to provide even more detailed and accurate information about air quality, helping users to make informed decisions about when and where to exercise and lead healthier lives.
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Actionable environmental data provides a myriad of opportunities to businesses, especially when combined with modern technologies like AI & ML. Leveraging environmental data, in this case, air quality and pollen, helps businesses improve brand loyalty, grow revenue, and enhance other business outcomes.
How Organisations Can Use Air Quality and Fire Data to Tackle Fire-Related Is...Ambee
As we head towards an uncertain world at the brink of a climate emergency, everything we do has a
repercussion. Even a small action could have a series of consequences that could worsen the situation.
Imagine living in a world so vulnerable that we need to think twice about whether the action we take
will affect the environment negatively. With this thought in mind, Ambee’s air quality and forest fire APIs have been designed to accumulate data to mitigate the risks caused by fire and air pollution.
5 Ways Air Quality Data is Helping the Healthcare Sector Innovate.pdfAmbee
Air quality is a major public health concern, and the healthcare sector is at the forefront of efforts to improve it. Air pollution can cause a variety of health problems, including respiratory illnesses, heart disease, and cancer. It can also exacerbate existing health conditions and make it difficult for people to recover from illness.
Enhanced fire data - Ambee’s new and improved forest fire APIAmbee
Explore Ambee's Forest Fire API to help prevent & mitigate fire impacts. Learn how businesses can use forest fire data to stay ahead of risks and make informed decisions.
Enhanced fire data - Ambee’s new and improved forest fire APIAmbee
Explore Ambee's Forest Fire API to help prevent & mitigate fire impacts. Learn how businesses can use forest fire data to stay ahead of risks and make informed decisions.
5 Ways Air Quality Data is Helping the Healthcare Sector InnovateAmbee
Discover how air quality data is helping the healthcare sector innovate with these 5 ways. Ambee explores the potential of air quality data and its applications in the healthcare sector. Read more to learn how air quality data is helping healthcare innovation.
Here’s How Air Quality Data Can Boost Your Fitness App's PerformanceAmbee
Ambee’s industry-validated air quality API is the best fit for this need since it provides real-time and hyperlocal air quality data for any lat long on Earth. Our data is analyzed from multiple sources, including our proprietary sensors, to give the highest level of accuracy.
The Role of Technology in Monitoring and Predicting Pollen and Air Quality Le...Ambee
Pollen and air quality data can be used to inform public policy decisions, help determine the best
time to exercise, and even aid in the diagnosis and treatment of respiratory illnesses. With the help of technology, it is now possible to map pollen and air quality levels in an efficient and cost effective manner. With Ambee's data, people everywhere can get a real-time understanding of their hyperlocal environment. This data is being used to power decisions by everyone from startups to Fortune 500 companies across the globe.
The Benefits of Air Quality Monitoring in Public Spaces.pdfAmbee
Air quality monitoring is the process of tracking the levels of pollution in your local public spaces. Pollutants in the air can impact your health and the health of those around you. An air quality API, is a tool that allows people to access data about air pollution.
Why do wildfires occur & how can we predict them?Ambee
They can do this by examining past wildfire data, preventing supply chain disruption, safeguarding factory and road workers' lives through live fire events, and predicting wildfire dataset risk around infrastructure and buildings.
The Ultimate Guide to Understanding Air Quality DataAmbee
Ambee is a network of air quality data that aims to make cities smarter, individuals healthier, and
air pollution decisions that are informed. Ambee's solutions anticipate and forecast air quality
with better accuracy and detail over low-cost sensors just on the market by combining satellite and
meteorological data with fine-grained IoT data.
Wildfires can occur anytime, anywhere, and are frequently brought on by human action or a natural occurrence like lightning. It is unknown how 50% of the wildfires dataset that has been reported got started.
ENVIRONMENT~ Renewable Energy Sources and their future prospects.tiwarimanvi3129
This presentation is for us to know that how our Environment need Attention for protection of our natural resources which are depleted day by day that's why we need to take time and shift our attention to renewable energy sources instead of non-renewable sources which are better and Eco-friendly for our environment. these renewable energy sources are so helpful for our planet and for every living organism which depends on environment.
Kinetic studies on malachite green dye adsorption from aqueous solutions by A...Open Access Research Paper
Water polluted by dyestuffs compounds is a global threat to health and the environment; accordingly, we prepared a green novel sorbent chemical and Physical system from an algae, chitosan and chitosan nanoparticle and impregnated with algae with chitosan nanocomposite for the sorption of Malachite green dye from water. The algae with chitosan nanocomposite by a simple method and used as a recyclable and effective adsorbent for the removal of malachite green dye from aqueous solutions. Algae, chitosan, chitosan nanoparticle and algae with chitosan nanocomposite were characterized using different physicochemical methods. The functional groups and chemical compounds found in algae, chitosan, chitosan algae, chitosan nanoparticle, and chitosan nanoparticle with algae were identified using FTIR, SEM, and TGADTA/DTG techniques. The optimal adsorption conditions, different dosages, pH and Temperature the amount of algae with chitosan nanocomposite were determined. At optimized conditions and the batch equilibrium studies more than 99% of the dye was removed. The adsorption process data matched well kinetics showed that the reaction order for dye varied with pseudo-first order and pseudo-second order. Furthermore, the maximum adsorption capacity of the algae with chitosan nanocomposite toward malachite green dye reached as high as 15.5mg/g, respectively. Finally, multiple times reusing of algae with chitosan nanocomposite and removing dye from a real wastewater has made it a promising and attractive option for further practical applications.
Microbial characterisation and identification, and potability of River Kuywa ...Open Access Research Paper
Water contamination is one of the major causes of water borne diseases worldwide. In Kenya, approximately 43% of people lack access to potable water due to human contamination. River Kuywa water is currently experiencing contamination due to human activities. Its water is widely used for domestic, agricultural, industrial and recreational purposes. This study aimed at characterizing bacteria and fungi in river Kuywa water. Water samples were randomly collected from four sites of the river: site A (Matisi), site B (Ngwelo), site C (Nzoia water pump) and site D (Chalicha), during the dry season (January-March 2018) and wet season (April-July 2018) and were transported to Maseno University Microbiology and plant pathology laboratory for analysis. The characterization and identification of bacteria and fungi were carried out using standard microbiological techniques. Nine bacterial genera and three fungi were identified from Kuywa river water. Clostridium spp., Staphylococcus spp., Enterobacter spp., Streptococcus spp., E. coli, Klebsiella spp., Shigella spp., Proteus spp. and Salmonella spp. Fungi were Fusarium oxysporum, Aspergillus flavus complex and Penicillium species. Wet season recorded highest bacterial and fungal counts (6.61-7.66 and 3.83-6.75cfu/ml) respectively. The results indicated that the river Kuywa water is polluted and therefore unsafe for human consumption before treatment. It is therefore recommended that the communities to ensure that they boil water especially for drinking.
Presented by The Global Peatlands Assessment: Mapping, Policy, and Action at GLF Peatlands 2024 - The Global Peatlands Assessment: Mapping, Policy, and Action
Climate Change All over the World .pptxsairaanwer024
Climate change refers to significant and lasting changes in the average weather patterns over periods ranging from decades to millions of years. It encompasses both global warming driven by human emissions of greenhouse gases and the resulting large-scale shifts in weather patterns. While climate change is a natural phenomenon, human activities, particularly since the Industrial Revolution, have accelerated its pace and intensity
Optimizing Post Remediation Groundwater Performance with Enhanced Microbiolog...Joshua Orris
Results of geophysics and pneumatic injection pilot tests during 2003 – 2007 yielded significant positive results for injection delivery design and contaminant mass treatment, resulting in permanent shut-down of an existing groundwater Pump & Treat system.
Accessible source areas were subsequently removed (2011) by soil excavation and treated with the placement of Emulsified Vegetable Oil EVO and zero-valent iron ZVI to accelerate treatment of impacted groundwater in overburden and weathered fractured bedrock. Post pilot test and post remediation groundwater monitoring has included analyses of CVOCs, organic fatty acids, dissolved gases and QuantArray® -Chlor to quantify key microorganisms (e.g., Dehalococcoides, Dehalobacter, etc.) and functional genes (e.g., vinyl chloride reductase, methane monooxygenase, etc.) to assess potential for reductive dechlorination and aerobic cometabolism of CVOCs.
In 2022, the first commercial application of MetaArray™ was performed at the site. MetaArray™ utilizes statistical analysis, such as principal component analysis and multivariate analysis to provide evidence that reductive dechlorination is active or even that it is slowing. This creates actionable data allowing users to save money by making important site management decisions earlier.
The results of the MetaArray™ analysis’ support vector machine (SVM) identified groundwater monitoring wells with a 80% confidence that were characterized as either Limited for Reductive Decholorination or had a High Reductive Reduction Dechlorination potential. The results of MetaArray™ will be used to further optimize the site’s post remediation monitoring program for monitored natural attenuation.
2. • Air quality impacts both our health and the environment, making it a
topic of significant concern and study. Air pollution, caused by various
natural and human activities, has become a pressing global issue, and
understanding it in depth is vital in safeguarding our planet and ensuring
a healthier future for generations to come. One of the major contributing
factors to poor air quality is PM2.5.
3. What makes particulate matter so important to
measure?
• Particulate matter (PM) is a mixture of solid particles and liquid
droplets found in the air. PM2.5 is a type of PM that is 2.5
micrometers or less in diameter. These particles are so small that they
can penetrate deep into the lungs and cause various health problems,
including respiratory infections, heart disease, and cancer.
• Outdoor air pollution, including PM2.5, was responsible for
approximately 4.2 million premature deaths worldwide in 2019. With
more than 90% of the world’s population living in places where the
WHO air quality guidelines levels were not met, it is now more
important than ever to measure PM2.5 and other pollutants.
4. To reduce the effect of PM2.5 pollution on health,
it is essential to measure it. Some benefits
include–
• Identifying areas with high levels of PM pollution, which can then be targeted for
clean-up efforts.
• Tracking trends and patterns in PM pollution over time.
• Developing and implementing products or policies to reduce PM pollution and
protect public health.
In this blog, we will cover the different methods of measuring PM2.5 and discuss
why it is now more important than ever to involve modern technologies in this
process.
5. The traditional way of measuring PM2.5
• The conventional way of measuring PM2.5 is to use a reference method
called the gravimetric method. This method involves collecting PM2.5
particles on a filter and then weighing the filter to determine the
concentration of PM2.5. The gravimetric method is accurate but it is
also tedious, time-consuming, and expensive, making it an inefficient
method.
6. New technologies for measuring PM2.5
In recent years, several new technologies have been developed for measuring PM2.5. These technologies are more
affordable and more accessible to use than the gravimetric method.
• Optical spectroscopy: One of the most promising new technologies for measuring PM2.5 is optical spectroscopy.
Optical spectroscopy uses light to measure the size and concentration of PM2.5 particles. Optical spectroscopy is
accurate and fast, and it can be used to measure PM2.5 in real time.
• Laser scattering: Another promising new technology for measuring PM2.5 is laser scattering. Laser scattering uses a
laser beam to measure the size and concentration of PM2.5 particles. Laser scattering is accurate and fast, and it can
be used to measure PM2.5 in real time.
• Beta Attenuation Monitor: The Beta Attenuation Monitor (BAM) is a widely used method for measuring airborne
particulate matter (PM) concentrations, specifically focusing on PM2.5 and PM10. The BAM measurement method is
based on the principle of beta attenuation, which involves the attenuation (reduction) of beta radiation as it passes
through a filter loaded with particulate matter. The beta radiation source used in the BAM is typically a radioactive
isotope, such as promethium-147 (Pm-147), which emits beta particles.
•
7. • The BAM method provides continuous real-time measurements of PM2.5 concentrations,
allowing for near-instantaneous data availability. It is widely used and recognized as a
standard method for regulatory compliance monitoring and research purposes.
• Particulate Testing: One of the most interesting modern methods is particulate testing.
Particulate testing is the process of measuring the concentration of particulate matter in a
given environment. Particulate matter is a mixture of solid particles and liquid droplets
suspended in the air. It can be generated from various sources, including combustion,
industrial processes, and natural sources such as dust and pollen.
8. Several different methods for particulate testing
include–
• Sieving: This method involves passing a sample of air through a series
of sieves with different mesh sizes. The particles trapped on the
sieves are then weighed to determine the concentration of
particulate matter.
• Impaction: This method directs a stream of air at a surface coated
with a sticky material. The particles in the air are then impacted onto
the surface and can be counted or weighed.
• Optical microscopy: This method uses a microscope to count and
measure the size of particles in a sample of air.
9. Newer methods for particulate testing include:
• Inductively coupled plasma mass spectrometry (ICP-MS): This method uses a high-
energy plasma to ionize particles in a sample of air. The ions are then separated and
measured by their mass to determine the concentration of different types of
particulate matter.
• Total suspended particulate (TSP): This method measures the total mass of
particulate matter in a sample of air.
The choice of particulate testing method depends on many factors, including the size
of the particles to be measured, the concentration of particulate matter in the air, and
the desired accuracy of the results.
10. What are the benefits of using new technologies
to measure PM2.5?
There are a number of benefits to using new technologies to measure PM2.5.
New technologies are more affordable and have no such maintenance costs. This makes it possible for
more people to measure PM2.5, which can help to improve air quality monitoring.
These are also easier to use than the gravimetric method. This makes it possible for people with less
technical expertise to measure PM2.5, which can help to expand the reach of air quality monitoring.
New technologies can be used to measure PM2.5 in real time. This allows people to take action to
protect their health when PM2.5 levels are high, making it highly beneficial. There are also data
accessibility and analysis opportunities on a personalized dashboard.
Data calibration and monitoring take only a few seconds with these devices.
11. How do we measure PM2.5 at Ambee?
Traditional methods for tracking air pollution have remained the same since the 1900s, which can be seen as
outdated and inefficient. These methods are not scalable and only cover a minimal portion of a city, leaving
the majority unmonitored. Despite these limitations, governments are compelled to utilize this technology
due to a need for alternatives.
At Ambee, we combine data from on-ground sensors, remote imagery from satellites, and other sources.
Our proprietary models measure, process, and analyze data from over a dozen sources, processing many
terabytes of data every day.
We also provide sensors to monitor city-wide air quality and emission levels. These devices use use low-cost
sensors for continuous AQ monitoring. These sensors work on the Non-Dispersive Infra-Red (NDIR) principle,
widely accepted as the industry standard at this level. Ambee’s sensors are extremely reliable, as proven in
tests and real-world scenarios, with uptime for all the devices at approximately 98.8%.
Learn more about Ambee’s IoT-Enabled sensor network here.
12. • To derive meaningful insights, we employ our proprietary artificial intelligence and
sophisticated data science and analytics methodologies. By harnessing the power of
these technologies, we can generate air quality data at a granular level, precisely
pinpointing air pollution information down to individual zip codes. This enables us to
provide a comprehensive and accurate representation of air quality, empowering
governments and communities to make informed decisions and take necessary
actions to address air pollution issues.
• We use globally valid data that follows US EPA standards to provide relevant
recommendations.
13. Measuring and monitoring particulate matter–
The future
As we know, new technologies are revolutionizing how we measure PM2.5 due to
their affordability and ease of use. As a result, they are making it possible for more
people to measure PM2.5, which can help to improve air quality monitoring and
protect public health.
The help of technologies like air quality sensors and a combination of different
methods to generate one highly accurate dataset to measure PMs is the best way
forward for the betterment of human and planetary health. This can help reduce
outdoor air pollution and initiate indoor air quality measurement for a safer future.