Indoor Air Pollution from Residential Stoves: Examining the Flooding of Particulate Matter into Homes during Real-World Use
- It presents a framework in order to determine real-world indoor PM exposure from the use of residential heating stoves.
- It can detect and identify the existence of peak indoor , , and PNC levels as a result of stove use.
- It analyses the results in relation to the DEFRA regulations and determines the extent of these emissions from a specific category of stoves; those that are certified as a ‘Smoke Exempt Appliance’ by DEFRA.
|Study||Year-Study Site||No. of Sampled Stoves||Lab-Conditions or Real-World?||Heating Unit Type and Fuel Acceptance||No. of Uses Analysis Based on|
|Traynor et al. ||1987-USA||4||Lab/Real-world hybrid||Wood stoves (3 ‘airtight’, 1 ‘non-airtight Franklin model’)||11|
|Allen et al. ||2009-Canada||15||Real-world (stove upgrade halfway through)||Wood stove (non-EPA-certified and EPA-certified)||Not provided (2 three-day samples taken over 6 days)|
|Noonan et al. ||2012-USA||21||Real-world (stove upgrade halfway through)||Wood stove (non-EPA-certified and EPA-certified)||Approx. 60 (1-4 samples taken from each home across 3 winters)|
|McNamara et al. ||2013-USA||50||Real-world||Wood stove (Non-EPA certified ‘older model’)||Not provided (4 separate 48h sampling visits over 2 winters)|
|Canha et al. ||2014 -Portugal||1||Real-world||Wood stove (‘slow combustion stove’)||1|
|Salthammer et al. ||2014-Germany||7||Real-world Wood stove (‘closed’)||6 Wood stove (‘open’)1||3 days for each stove|
|Piccardo et al. ||2014-Italy||9||Real-world||Wood stoves||183|
|Semmens et al. ||2015-USA||96||Real-world||Wood stoves (‘older models’ without ‘modern control features focused on emission reduction’)||192 (each stove used twice)|
|Vicente et al. ||2015-Portugal||1||Lab-conditions||Wood stove (‘stainless steel with a cast iron grate’)||Not provided|
|Mitchell et al. ||2016-UK and Ireland||1||Lab-conditions||Multi-fuel stove (‘fixed grate stove with a single combustion chamber’)||8|
|Wang et al. ||2020-China||5||Lab-conditions(1) Real-world(4)||Coal stoves (Real world—‘steel stoves, cylindrical burning chamber, connected to a chimney’)||Not provided|
|Vicente et al. ||2020-Portugal||2||Lab-conditions||Open fireplace and wood stove||7 (4 open fire, 3 wood stove)|
|Chakraborty et al.||2020-UK||20||Real-world||DEFRA-certified wood (14)- DEFRA-certified multi-fuel (5)- Defra-compliant open fire (1)||260|
2. Materials and Methods
2.1. Sampling Area and Study Design
2.2. Sensor Validation and Correction: Accuracy, Evaluation and Limitations
- Raw data, including , , Temperature (T), and Relative Humidity (RH), were received every 160 s. This was converted to hourly averages in order to match the reference station data, because only hourly reference data are publicly available.
- The hour average was excluded if less than 90% of the measurements were available in that hour average.
- Humidity Correction: concentrations can be relatively high from low-cost PM sensors at high RH levels. The hygroscopic growth of particles at high humidity, along with mist and fog particles, makes it detectable as particulates, as previously reported [39,40]. A Nephelometer, such as PMS5003, measures particulates based on light scattering principle. The particulates’ refractive indices are dependent on relative humidity  and, thus, affects the sensor readings. While ambient temperature directly has a very limited role in sensors performance  (apart from extreme temperature), it affects the measurements indirectly. Jayaratne et al.  reports that, when the ambient temperature reaches the dew point temperature, the conditions become suitable for the formation of fog droplets in the air and fall within the detection size of such sensors. Figure 3 presents an example of the relationship between RH values and PM2.5 data from PMS5003 collocated. A Humidity-based bias correction approach was taken, as described here , while using the -Köhler theory . The hygroscopic growth factor g (RH), as defined in Equation (1), where is the diameter of the dry particle and is the diameter of the particle at a given RH value.RH dependence  was established while using Equation (2), as follows:Two additional PMS5003 have also been collocated at the same station permanently since 23rd April 2019 have been used to ensure correction factor accuracy. A conditional Quartile plot in Figure 4 below uses the corresponding values for both reference and low cost sensors, splitting the values into evenly spaced bins. For each low cost sensor value bin, the corresponding reference sensor values are identified and the median, 25/75t,h and 10/90 percentile (quantile) are calculated for that bin. The data are plotted in order to show how these values vary across all bins. The blue line shows the results for a perfect model i.e., zero error between low cost PMS5003 sensor and the reference Palas FIDAS 200 sensor. In the plot in Figure 4, the red line shows that the LCS tends to slightly over-report for (NMB ≈ 0.2–0.3).
- Concentration Range Correction: a correction was applied based on the relationship between pollutant concentration range and sensor performance. Multivariate Linear regression model were used in order to establish the relationship. Palas Fidas 200: is used as the dependent variable and PMS5003 sensor data: , T, and RH as predictors, as shown in Equation (3)., and are calculated by training with the model generated. To note, is not used here, as it is obtained from the previous step.
- Evaluation of LCS: PMS5003 corrected data are evaluated by comparing to the Palas Fidas 200 values in the holdout data set. From the field evaluation through collocation between January–April 2020, PMS5003 showed high linear correlation with reference instrument with value 0.81 for the hourly averaged data. This is an improvement in accuracy when compared to the findings from previous studies on evaluating Plantower sensors [10,46] with values lying between 0.71–0.77 for PMS5003 without applying any correction factors. The inter-sensor comparison showed a high correlation, with an value between 0.98–0.99. Figure 5, below, shows the scatter plot between the reference and corrected PMS5003 sensor.
2.3. Monitoring Outdoor Air Quality and Adjusting for Weather: A Generalized Boosted Regression Model
2.4. Data Processing and Storage
- The ability to ingest a high volume of time series data with dynamic data from the sensors.
- The ability to return this time series data with basic querying parameters such as sensor ID and timestamps.
2.5. Data Analysis
- A and B represent the Control and Experimental group.
- and represent the means of groups of samples A and B, respectively.
- and represent the sizes of group A and B, respectively.
- and are the standard deviation of the two groups A and B, respectively.
2.6. Study Limitations
3. Results and Discussion
3.1. Increase in Indoor Pollution Levels during Stove Use
3.2. Indoor Outdoor Interface: Average Indoor Levels Are Higher and Weakly Correlated with Outdoor Average PM Levels
3.3. Hourly Peak PM Average Higher than Daily PM Average
3.3.1. Hourly Peak Average PM Has a Moderate Correlation to the Pieces of Fuel Used
3.3.2. Hourly Peak Averages Illustrate a Moderate Correlation with Duration of Use
Conflicts of Interest
|n||the number of complete pairs of data.|
|FAC2||fraction of predictions within a factor of two|
|MGE||mean gross error.|
|NMB||normalised mean bias.|
|NMGE||normalised mean gross error.|
|RMSE||root mean square error.|
|r||Pearson correlation coefficient.|
|COE||the Coefficient of Efficiency|
|IOA||the Index of Agreement|
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|gm||0–11||12–23||24–34||5–41||42–46||47–52||53–58||59–64||65–69||70 or more|
|Coefficient of Variation||0.39||0.69||1.14||0.90||1.14||0.94||0.67|
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Chakraborty, R.; Heydon, J.; Mayfield, M.; Mihaylova, L. Indoor Air Pollution from Residential Stoves: Examining the Flooding of Particulate Matter into Homes during Real-World Use. Atmosphere 2020, 11, 1326. https://doi.org/10.3390/atmos11121326
Chakraborty R, Heydon J, Mayfield M, Mihaylova L. Indoor Air Pollution from Residential Stoves: Examining the Flooding of Particulate Matter into Homes during Real-World Use. Atmosphere. 2020; 11(12):1326. https://doi.org/10.3390/atmos11121326Chicago/Turabian Style
Chakraborty, Rohit, James Heydon, Martin Mayfield, and Lyudmila Mihaylova. 2020. "Indoor Air Pollution from Residential Stoves: Examining the Flooding of Particulate Matter into Homes during Real-World Use" Atmosphere 11, no. 12: 1326. https://doi.org/10.3390/atmos11121326