Performance Comparison of LiDAR-Based and Non-LiDAR PM2.5 Air Quality Monitors
Advisor: Madame Laquidara
Internship Location: SUW Sensors, Energy and Automation Lab (SEAL), Department of Electrical and Computer Engg., University of Washington
Onsite Mentor: Alexander Mamishev
The World Health Organization (WHO) estimates that 7 million premature deaths occur globally each year as a result of air pollution. Because of their significantly greater pollution levels, urban places are far more dangerous for their inhabitants. In order to help public officials better manage air quality challenges, my project intends to improve real-time monitoring of PM2.5, an air pollutant, across urban environments by creating a system that integrates LiDAR with air quality sensors. In contrast to traditional stationary air quality monitors, this device would provide real-time data on concentrations of PM2.5 with a higher spatial resolution to assist city planners in mitigating pollution exposure.
The research will involve a multi-phase methodology, beginning with a literature review on LiDAR’s environmental applications, then sensor selection, prototype development, and testing the sensor in locations in the city of Fremont with different pollution levels. The study will evaluate how accurate the system is in detecting the concentration of PM2.5 in the air and analyze how much potential it Has to improve current air quality monitoring approaches. The findings could contribute to the development of a novel real-time urban pollution monitoring network that enhances public health and sustainability efforts.
Project Blog
Post 1: Blog 0
Hi everyone! Welcome to my LiDAR-based PM2.5 monitoring sensor blog! I will be working to build a sensor that tracks the concentration of PM2.5 in the air and will be documenting my journey through these blog posts!
“What is a LiDAR-based sensor?” you may ask. Well, LiDAR stands for Light Detection and Ranging, and it is most commonly used for spatial mapping and autonomous driving. How LiDAR works is that it emits a laser pulse that bounces off an object and returns to the sensor, allowing it to calculate distances with precision. However, LiDAR has not yet been extensively explored in terms of monitoring air pollutants, so in my senior project, I aim to develop an innovative and adaptable solution that can do just that.
To provide some context for the problem I am trying to solve, the World Health Organization (WHO) estimates that 7 million premature deaths occur globally each year as a result of air pollution. Because of their significantly greater pollution levels, urban places are far more dangerous for their inhabitants. In order to help public officials better manage air quality challenges, my project intends to improve real-time monitoring of air pollution across urban environments by creating a system that utilizes LiDAR to monitor for the concentration of PM2.5 in the air.
Why me?
I’ve always been interested in addressing environmental problems, as I believe the health of humans and the environment are directly linked to each other. After witnessing the tremendous amount of smokers in China and seeing my grandmother’s health deteriorate due to mouth cancer, possibly because of her secondhand exposure to smoke when she was younger, my passion for improving the health of the environment and humans in parallel was ignited even more.
Soon after, I joined Professor Alexander Mamishev’s lab at the University of Washington. My first project entailed engineering a stationary LiDAR sensor for social-distancing from unsafe environments. Though not directly related to helping the environment, this project gave me insight into how sensors could be used to protect human health, which is the core of my project. It also taught me the engineering, physics, and mathematics expertise that I will use during this project.
To build this LiDAR-based PM2.5 monitoring sensor, I’m working with Professor Mamishev, who has decades of expertise in the electrical and computer engineering field, and Madame Laquidara, my French teacher since 9th grade and internal advisor.
I can’t wait to go on this journey and bring all of you with me! See you at my next blog!
Post 2: Blog 1
Welcome to my Week 1 Senior Project Blog!
This week, I focused on laying a strong research foundation to understand how I can best leverage LiDAR for air quality monitoring purposes.
I began by examining existing research on LiDAR’s applications to figure out how it is used for environmental monitoring.
I first read “Urban Air Pollution Monitoring Using Scanning Lidar.” This study provided detailed insights into how scanning LiDAR systems have been used to detect pollutant concentrations across urban landscapes. I learned that LiDAR’s ability to emit laser pulses and measure the backscatter signal from these pulses can be used to detect the presence of airborne particles, including PM2.5, which is what my developed sensor will detect. However, I also found out that LiDAR has limitations such as being more sensitive to weather conditions and not being able to detect very fine particles, and PM2.5 is a very fine particle.
As a result, I began researching various air quality sensors that measure PM2.5. I focused on laser scattering sensors, which measure particulate matter by detecting the amount of light scattered by airborne particles. They are highly sensitive to fine particles, which make them ideal for PM2.5 monitoring.
As a LiDAR approach can not simply detect PM2.5 by itself, it must be combined with another air quality sensor to accurately detect the concentration of PM2.5 in the air. LiDAR is useful for detecting particle presence and has the ability to cover a wide detection area, while laser scattering sensors have high accuracy in detecting PM2.5 concentrations and can differentiate between fine particles.
In order to learn how to integrate these sensors, I started reading “The Art of Electronics.” I reviewed basic circuit concepts such as voltage and current, which are essential for designing sensor circuits. I also read a chapter on op-amps and analog signal processing. This chapter was really useful because I plan to use op-amps for amplifying the signals from the air quality sensors. I noted down how to configure different op-amp circuits such as inverting and non-inverting amplifiers.
One challenge I faced was not being able to figure out how to synchronize the data from LiDAR and the laser scattering sensors. I realized that accurate timing and data processing are a must in order to reliably detect the concentration of PM2.5, so I will explore microcontroller integration in the next phase.
Stay tuned for more updates on my work!
Post 3: Blog 2
Welcome to my Week 2 Senior Project Blog!
This week, I made a few changes to the trajectory of my senior project. After reading more about sensor integration in “The Art of Electronics” and conversing with my professor, we came to the conclusion that creating a sensor prototype in three months is unachievable.
Now, instead of creating a LiDAR sensor that can detect the concentration of PM2.5 in the air and testing it, I will be writing a research paper that compares a LiDAR sensor that detects PM2.5 vs. a non-LiDAR sensor that detects PM2.5.
As a result, this week, I focused on selecting the specific LiDAR sensor I would use for the testing. After much analysis, I chose the GQ Air Quality Monitor Air-760. There are multiple reasons as to why I chose this sensor. The first is because of its relatively cheaper price. Priced at $188, it is much more affordable than advanced LiDAR technology, which is simply out of budget for many people. Additionally, although it’s cheaper, it still works with good quality, albeit not the best quality.
After, I did some research into the advantages and disadvantages of LiDAR. The main advantage of LiDAR is that it can detect both small and large particles, which means it can detect PM2.5 particles of all sizes, while non-LiDAR sensors struggle to do this. Additionally, another advantage of LiDAR is that it works in low visibility conditions. LiDAR systems use backscattered laser light, so they are not as affected by air conditions such as smoke.
However, in extreme conditions such as heavy rain or dense fog, LiDAR performance decreases dramatically. These conditions can scatter the laser beam, reducing the accuracy of the readings. Another disadvantage of LiDAR is that they generate a large amount of high-resolution data, which means that they require powerful computing resources and advanced algorithms for real-time analysis. They also require an unobstructed path to the target object.
I haven’t chosen the non-LiDAR PM2.5-detecting sensor yet, but I will get to that next week.
Stay tuned for more updates on my work!
Post 4: Blog 3
Welcome to my Week 3 Senior Project Blog!
This week, I first focused on choosing a non-LiDAR PM2.5-detecting sensor. After much analysis, I decided on the Temtop Air Quality Monitor. There are several reasons as to why I chose this sensor. First of all, the integrated laser particle sensor reduces production costs by 38%, which makes it affordable but also high-quality. Additionally, the device also monitors temperature and humidity, which are other factors that must be taken into consideration when detecting the concentration of PM2.5 in the air.
The technology behind the Temtop Air Quality Monitor is a laser particle sensor, which means it utilizes laser scattering. A laser beam is directed into an air chamber, and particles in the air scatter the light based on factors such as size and concentration. Then, a photodetector measures the intensity of scattered light, which is based on particle size and density. Lastly, internal algorithms get the concentration of PM2.5 based on light scattering patterns.
This laser scattering technology has several advantages over LiDAR technology. First, it has high accuracy when detecting fine particles such as PM2.5 because PM2.5 particles are small enough to scatter visible laser light efficiently. Additionally, it does not require line-of-sight. LiDAR sensors work by bouncing laser pulses off objects in an open environment.
Temtop’s enclosed sensor does not need an unobstructed path to detect PM2.5, which makes it good for monitoring air quality in confined spaces.
One disadvantage of laser scattering technology is its ability to only measure air quality at the sampling point, not across a large area. Additionally, it may require airflow for accurate readings because the particles need to pass through the detection chamber.
I just received both sensors from Amazon, so next week, I will take them apart to inspect their inner workings and begin testing them. I will also begin writing the research paper.
Stay tuned for more updates on my work!
Post 5: Blog 4
Welcome to my Week 4 Senior Project Blog!
This week, I received the the GQ Air Quality Monitor Air-760. Attached below is a picture of it.
I calibrated the sensor’s settings, and now, it tracks the concentration of PM2.5 in the air. I plan on collecting data with the sensor over a period of several weeks.
The Temptop Air Quality Monitor has not yet arrived, but I will begin testing it as soon as it does.
Next, I began writing the introduction of my research paper. I plan on having several sections in the paper: Introduction, Technical Background, Experimental Setup, Results, and Conclusions. The abstract of the paper will be written after the paper is fully finished, as it is a summary.
The introduction of my paper currently contains insight into why it is important to track air pollution, more specifically PM2.5. It also mentions existing methods for monitoring PM2.5. Next, I briefly talked about the different workings of the LiDAR-based PM2.5-detecting sensor and the non-LiDAR-based PM2.5-detecting sensor, highlighting advantages and disadvantages for each. I also talked about why it’s necessary to compare these two different technologies (e.g. accuracy and cost). Lastly, I stated the purpose of my study, which is to compare the performance of a LiDAR-based sensor and a non-LiDAR sensor in measuring PM2.5 concentrations.
Next week, I will begin writing the technical background section of the paper. I will take apart each sensor to fully understand the inner workings and write down what technology they use, such as the type of chip.
Stay tuned for more updates on my work!
Post 6: Blog 5
Welcome to my Week 5 Senior Project Blog!
This week, I focused on doing research and writing for the technical background section of my research paper.
First, I moved part of the introduction - the portion on why air quality monitoring is important - to the beginning of my technical background. Then, I explained in more detail about LiDAR technology and what it does in air quality monitoring. Specifically, LiDAR systems emit laser pulses and measure the backscatter from particles in the air, which helps determine the concentration and presence of pollutants such as PM2.5. I also talked about the advantages and disadvantages of LiDAR, such as detection of particles of all sizes and high data requirements.
Next, I discussed how non-LiDAR sensors for PM2.5 detection operate. Non-LiDAR sensors operate by passing a laser beam through a detection chamber. As airborne particles, including PM2.5, pass through the beam, they scatter the light. I also navigated the advantages and disadvantages of non-LiDAR PM2.5-detecting systems, such as high accuracy for small particles and dependency on airflow.
Lastly, I explored the need to compare LiDAR and non-LiDAR sensors in PM2.5-detection. Both technologies have their advantages in terms of detecting PM2.5, but their performance in detecting the pollutant can differ a lot. LiDAR has a broad detection range and can measure particulate presence across a large area, while non-LiDAR sensors are better at targeting a specific area. It’s imperative that we understand these differences so we can select the right technology based on the specific use case. Additionally, factors like cost need to be considered when evaluating their overall effectiveness.
Stay tuned for more updates on my work!
Post 7: Blog 6
Welcome to my Week 6 Senior Project Blog!
This week, my Temtop Air Quality Monitor arrived. I began tracking the concentration of PM2.5 with it, comparing it to the GQ Electronics PM2.5 detection. I noticed that the Temtop monitor tended to display more stable levels of PM2.5 detection, hovering around 4 ug/m^3, while the GQ monitor varied more, ranging from 3 - 7 ug/m^3. I haven’t collected enough data to make a conclusion yet.
As for the technical background section for the Temtop Air Quality Monitor, the instructions specifically stated to not take it apart, so I contacted my external advisor for more insight into what type of chip they use, and more specifics about their technology use as well, as he is well-versed in this area. I will send an email to the company as well.
For the remainder of this week and the next week, I will be collecting data every day. Then, I will begin writing the experimental setup and results section of my paper.
Stay tuned for more updates on my work!
Post 8: Blog 7
Welcome to my Week 7 Senior Project Blog!
This week, I focused on collecting data from the sensors. There was no visible change in the concentration of PM2.5 detected every day, but I did notice that there were fluctuations of up to 3 ug/m^3. To get a feel of how the sensors work in outside environments, I took both of the sensors outside yesterday, but I haven’t collected enough data to make a conclusive statement.
I also began writing the experimental background section of my research paper - I took pictures of the sensors’ placement and mentioned how many cm the desk that the sensors were placed on is above the ground. This information is necessary because when writing the experimental background of a research paper, it’s imperative to include all details. I also plan on using a light detector app on my phone to test the lux of the room that my sensors are placed in at different times of the day to see if there may be a correlation between lux and PM2.5 concentrations.
I plan on ending my data collection in the middle of next week or at the end of next week, and then I will fully focus on writing the research paper.
Stay tuned for more updates on my work!
Post 9: Blog 8
Welcome to my Week 8 Senior Project Blog!
This week, I finished writing the abstract, introduction, and technical background of my research paper, and I’m in the middle of writing the experimental setup. My research paper will have a few more sections after those: results, conclusion, future work, acknowledgement, references. The paper is currently around 1.5 pages, and I plan for the full research paper to be around 3-4 pages.
I’m almost done with my data collection and plan to end it off this weekend. I’ve also begun planning out my research poster.
Stay tuned for more updates on my work!
Post 10: Blog 9
Welcome to my Week 9 Senior Project Blog!
This week marked a significant milestone in my senior project journey! I finished writing the experimental setup section of my research paper, where I detailed the equipment and procedures I used to conduct my experiments. In addition, I also officially completed my data collection. I am currently in the process of generating the visualizations and graphs that will be shown in the Results section of the paper.
Stay tuned for more updates on my work!
Post 11: Blog 10
Hi everyone! Welcome to my Week 10 Senior Project Blog, also the final one that will wrap up my senior project.
This project has taught me so much about LiDAR and light scattering, along with how these technologies can be used in the field of environmental monitoring. The knowledge and skills I’ve gained in the past 10 weeks have solidified my desire to pursue a career in the sector of human health - whether that’s through engineering or another field entirely.
In terms of my project progress, I’m in the final stages of wrapping up my research paper. I’ve decided to add in one more experiment involving an air purifier to provide more conclusive evidence, so I still need to write about the results from that experiment. To be completely honest, there are many factors in my project, such as wind speed, that could have influenced my experiments, but ultimately, 10 weeks was not enough to do a full-on case study. However, I’m satisfied with the graphs I’ve created from my data because they still showcase important trends that can be used to inform people about PM2.5 concentrations inside or outside their house.
My goal for this project was ultimately to help consumers decide which sensor is better for their use case - a more expensive $200 LiDAR sensor or a cheaper $30 light scattering sensor. There are so many different consumers who will need to buy PM2.5-detecting sensors, whether that’s people with asthma, business owners, or more, and it’s imperative to weigh the pros and cons because not everybody can buy a more expensive sensor. If the cheaper one still provides accurate data, then it’s not necessary to buy the more expensive one. I’m happy with the results of my project because I believe I’ve successfully addressed this goal.
Thank you for following me along on my journey! Remember to always be conscious of the pollution near your area, as long-term exposure to polluted areas can cause serious harmful health effects.