As a component of air pollution, particulate matter with an aerodynamic diameter that is equal to 2.5 μm or less (
) has long been linked to adverse health effects. In terms of mortality, it causes seven-million deaths per year [1
]. In terms of health effects, it causes inflammation and oxidative stress, which compromises pulmonary immunity and increases the susceptibility to infection [2
]. As these particulates can move into every organ in the body, the illnesses that are associated with their presence range from lung cancer, bronchitis, and other respiratory infections, through to strokes, dementia, and Parkinson’s disease [3
]. Effects such as these are particularly pronounced for children, pregnancies, and the elderly [4
]. While much research focuses on particulate emissions that are generated by industry and vehicles, in the United Kingdom (UK) the primary source for
is the domestic burning of wood and coal for heating [5
]. Government estimates suggest that one in twelve UK homes is using residential stoves [6
] and, in doing so, causing 38% of the nation’s
]. Growing in popularity, UK industry data suggest that stove sales are running between 150,000 and 200,000 units per year, with over one million being sold between 2010 and 2015 [7
]. Several reasons have been posited for this, including perceived lower fuel costs where wood or biomass is recovered locally, particularly where this intersects with fuel poverty, with residential stoves becoming a lifestyle choice for those who already have a primary source of heating in their home [8
], and the perception that wood burning stoves are low-carbon, because they can use renewable fuels [9
]. Much of the existing literature on these residential stoves focuses on their efficiency [10
] and outdoor emissions [12
], with many also deploying monitoring equipment in order to establish the indoor PM emissions that originate from their use. Early work by Traynor et al. [15
] measured indoor emissions from four wood burning stoves, finding that all of the stoves emitted particles indoor at some point during use. Canha et al. [16
] found that wood burning used to heat one school classroom in rural Portugal contributed high levels of
to the indoor environment. Semmens et al. [17
] examined 98 stoves over 48 h, finding average indoor
concentrations to exceed World Health Organisation ambient air quality guidelines and approach the United States Environment Protection Agency (U.S. EPA) 24-h standard equivalent. Piccardo et al. [18
] tested indoor air emissions from nine stoves, finding indoor air pollution to be consistent with errors in self-installation and mismanagement. Wang et al. [19
] tested one stove under lab conditions and four stoves in real-world settings. The number of tests conducted, or the real-world measurements taken, are unclear, but the study concludes that different emissions occur at different points during the burn cycle. Vicente et al. [20
] tested one open fire and one wood stove under lab conditions, finding that the
levels increased 12-fold for the former and 2-fold for the latter during operation. Allen et al. [21
] upgraded stoves in 15 houses in order to understand the extent to which stove design can improve indoor air quality, finding that no consistent improvement occurs. Table 1
summarises this literature. While adding to understandings of indoor stove emissions, this body of scholarship also exhibits several limitations.
First, existing studies tend to judge indoor stove emissions against official average exposure guidelines [22
]. This is a dominant approach in air quality research, but it serves to obfuscate emission ‘peaks’ by averaging them out of the results. For instance, while Semmens et al. [17
] found that the ‘reported number of times the wood stove was opened was not associated with
or any particle size fraction’, this judgement was made in the context of a 48h average. This is problematic because epidemiologists are increasingly recognising that exposure to high intensities of PM over much shorter periods of time—hours rather than days—is linked to a range of health issues [23
]. Indeed, Lin et al. [27
] found a significant association between hourly peak
and mortality rates across six Chinese cities. Similarly, a systematic review of 196 articles found a positive relationship between short term PM exposure and cardiovascular, respiratory, and cerebrovascular mortality [28
]. Several existing studies report stoves emitting peaks indoors, but these are either observed under controlled conditions [15
] or have few real-world users or uses from which to derive data [19
Second, the number of stove uses upon which conclusions are drawn is highly variable (see Table 1
). This is less of an issue with lab-based testing, as the circumstances of use can be tightly controlled. However, low frequencies of use pose a challenge for studies into real-world emissions because one instance of stove management may not be identical to another. Relatedly, participants may actively change their behaviour if aware they are being observed. Known as ‘participant reactivity’, this can be produced by researchers through obvious and repeated intervention into a social setting. In order to minimise this influence and more accurately ascertain what indoor emissions are occurring through normal use, the sampling of a greater number of stove uses over a longer period of time, and without obvious researcher intervention in the social setting, is required.
Third, existing studies are not clear about the standard of stove being tested. The fuel accepted is outlined and the stove described, albeit inconsistently so (see Table 1
), but the design regulations to which the stoves adhere, if at all, tend not to be detailed. This makes it difficult to generalise findings to categories of stove that share fundamental design features. Where stove standards are described, those chosen tend to have been approved by regulators outside the UK. For instance, [17
] have focused on stoves that are approved by environmental regulators, but these are limited to the USA and Canadian contexts. Taken together, this relationship between indoor emissions and UK-specific regulations that govern stove design and testing requires investigation.
Fourth, few of the existing studies examine Ultra Fine Particles (UFP), which are defined as particles with a diameter of less than 100 nm, or Particle Number Concentration (PNC), which is defined as the total number of particles measured per cubic centimeter in a given sample. Measuring PNC along with the regular mass concentration measurements of
is important because PNC and
are not representative of each other [32
], with Pearson’s r lying between 0.09–0.64 and high levels of
not necessarily causing high levels of PNC or vice versa. Therefore, measures that are taken to reduce or regulate
may be different to those that are needed to tackle the problem of increasing PNC. Indeed, Penttinen et al. [33
] found a stronger negative association between PNC and peak expiratory flow (PEF) than
amongst asthmatic children. Therefore, UFP may pose a substantial health risk since PNC exposure increases remarkably in the smallest size fractions.
When considering these limitations, this study has four aims. First, it seeks to determine real-world indoor PM exposure from the use of residential heating stoves over 30 days. This period was chosen to increase the number of uses from which data could be derived without instructing participants to use their stoves, minimise intrusion into the research setting, and more accurately capture ‘real-world’ use. Second, it detects and identifies the existence of peak indoor
levels as a result of stove use. Third, it seeks to clarify whether the level of indoor air pollution is originating from indoor or outdoor sources. Finally, it seeks to determine the extent to which these emissions are coming from a specific category of stoves; those that are certified as a ‘Smoke Exempt Appliance’ by the UK’s Department for Environment, Farming, and Rural Affairs (DEFRA). These stoves are modified in order to restrict incoming air and limit smoke produced from combustion, differentiating them from the older equipment of focus in Semmens et al. [17
]. If a stove passes the official testing process [34
], they are certified to be exempt from the Smoke Control Area regulations covering most of the UK’s towns and cities. However, this testing is limited to measuring outdoor air pollution via flue emissions and heat output; none of the applicable standards that are required by DEFRA are concerned with indoor PM emissions from stoves (see PD 6434: 1969; BS 3841: Part 1: 1994; BS 3841: Part 2: 1994). Even the latest ‘EcoDesign’ standards, which call up EN 16510:2018, do not introduce testing for indoor emissions. Indeed, when taken together, the DEFRA testing regime rests on a baseline assumption that stoves do not pollute indoors, or only do so when a fault is present. The results of this study test the validity of that foundational assumption. Taken together, this work makes three core contributions:
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.
In making these contributions, the study seeks to determine whether health risks are posed during normal operation and, in turn, whether DEFRA testing standards need modification in light of this reality.
The remainder of this paper is organised, as follows. Section 2
describes the experimental framework along with sensor calibration and evaluation in Section 2.2
. Section 3
presents the findings and analysis, which is followed by the conclusion in Section 4
Overview of Existing Literature that has Monitoring Indoor Pollution from Residential Heating Stoves.
Overview of Existing Literature that has Monitoring Indoor Pollution from Residential Heating Stoves.
|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 |
3. Results and Discussion
summarises the daily
mean, and hourly peak
mean from 20 households and 260 stove usages, along with the statistical analysis and distribution. Data on the average pieces of fuel per use (FP) and kindling per use (KP), along with the average duration of use, have also been presented. The hourly indoor mean
concentrations that were observed during stove usage ranged from 2.27
, respectively, with a high coefficient of variation 0.9 for
and 0.94 for
. The hourly PNC average that was observed indoors in the particle size range (0.3–1
m diameter) was 2607 particles/0.1 litre (L) of air when each stove was used, but the hourly peak PNC average observed was 4345 particles/0.1 L with an hourly maximum of 9978 particles/0.1 L. The average number of fuel pieces (9.58 wooden logs) and kindling (8.37 pieces) used varied significantly between the households, with a coefficient of variation 0.69 and 0.67, respectively. The average duration of use was approximately 4 h, with most households using their stove between 6 pm and 10 pm.
3.1. Increase in Indoor Pollution Levels during Stove Use
The findings indicate that average indoor (mean = 12.21 g/m SD = 10.36, 95%CL: 8.16, 12.68) and (mean = 8.34 g/m SD= 7.64, 95%CL: 5.29, 9.42) are higher when the stoves are lit when compared to the period in which they are not in use with levels (mean = 4.12 g/m SD= 3.61, 95% CL: 2.82, 4.82), and levels (mean = 2.54 g/m SD= 2.61, 95%CL: 1.59, 3.04). Statistical analysis estimates that the difference in concentrations between these two groups is significantly different for both (Welch’s t(57.0448) = , p < 0.0001) and (Welch’s t(56.6291) = , p < 0.0001).
The analysis in the three quartiles—(i) <25 percentile, (ii) >25 <75 percentile, and (iii) >75 percentile, representing low, medium, and peak concentrations, showed an increase for (223.92%, 241.23%, 127.84%) and (254.38%, 238.02%, 209.32%). The overall average concentrations were higher for by 196.23% and by 227.80% when used.
density rug plots show the distribution and levels of
for users. Figure 11
compare the control group’s indoor pollution levels with the experimental group. For reasons of visualisation, scaling the x-axis in the graph (see Figure 10
) is limited to 60
reveals that the levels of PM that people are exposed to can vary, with a maximum peak average of 47.60
. While calculating the averages smooths the graph, these findings demonstrate that some users are exposed to maximum values of up to 160
. Control users experience much lower indoor particulate levels when their stoves are not lit when compared to users that do, as indicated by Figure 11
In Figure 10
, comparing the concentration levels between usage and non-usage days for the control group also illustrates an increase for
(139.52%, 327.48%, 320.66%) and
(132.16%, 413.11%, 366.56%) when stoves are used. The overall average concentrations were higher for
by 432.91% and
3.2. Indoor Outdoor Interface: Average Indoor Levels Are Higher and Weakly Correlated with Outdoor Average PM Levels
The average indoor
levels are higher (mean = 12.21
SD = 10.36, 95%CL: 8.16, 12.68) than the outdoor
levels (mean = 7.99
SD= 5.51, 95%CL: 3.60, 8.93) during stove usage. From Figure 12
, below, it is clear that indoor and outdoor values vary significantly between 10–45
concentration levels. This variation is because the mean and hourly peak indoor PM lies within this range and, thus, the indoor levels are much higher than the corresponding outdoor levels. Further analysis of average indoor and outdoor
levels indicated a weak correlation (
= 0.19) between them, which suggests that outdoor air quality is not a driving factor behind the high indoor pollution levels that were seen during stove usage.
While we acknowledge that indoor levels can impact outdoor air quality, no measurements were taken from the chimney/flue. The air quality sensor outside the house indicates immediate outdoor air pollution levels and, thus, it is difficult to measure any leakage at the interface. Future research studies should focus on indoor air pollution and its influence on outdoor air quality in order to address this limitation of our study.
3.3. Hourly Peak PM Average Higher than Daily PM Average
The analysis of Table 3
shows hourly peak
is strongly correlated with daily mean
(r = 0.75). Statistical analysis shows that the hourly peak mean
, 95% CL:18.38, 37.77) and
, 95% CL:12.04, 28.30) are significantly higher than the daily mean
, 95% CL: 8.16, 13.68) and
, 95% CL: 5.29, 9.43) by 123.91% and 133.09%, respectively. Hourly
peak mean and the daily mean concentrations varied between households with the minimum and maximum, being 19.2
There exists high variation in exposure concentrations, concerning both short peaks and daily levels. This characteristic is related to the "real-world" nature of the study. The research diary tool provided data on not only the amount of fuel and kindling pieces used, but also their type. On average, participants used 9.58 pieces of solid fuel and 8.32 pieces of kindling per use. The number of fuel pieces used varied between a minimum of seven to a maximum of 40, while kindling varied between a minimum of one and a maximum of 32. All participants used dried and seasoned logs, but the sizes varied. There was also a diversity of kindling used, taking the form of firelighters, newspapers, balls of paper, twigs, sawdust, packing cardboard, greeting cards, and even empty egg boxes. Echoing the findings of existing studies [20
]. This means that the same wood burner may emit different levels of indoor air pollution depending on the quantity and type of fuel and kindling used. While suggesting a link between indoor air pollution and fuel quantity, and type of fuel and kindling, following other studies in the next section, demonstrates that this is actually linked with the stove door being opened.
Epidemiology studies and policymaking are focused around hourly average concentration monitoring by regulatory air quality stations. This leads to the omission of short-term high exposure through the “flooding” of indoor spaces with
. Very few studies have reflected on short term peak concentration exposure. Lin et al.’s study [27
] associated increased risk factors with hourly peak concentrations of
. Similarly, Delfino et al. [53
] associated peak PM levels with Asthma attacks in children, but in outdoor environments. Therefore, the present study encourages future researchers to study the occurrences and effects of relatively short-term peak PM exposure on human health.
3.3.1. Hourly Peak Average PM Has a Moderate Correlation to the Pieces of Fuel Used
While Table 3
indicates a weak correlation between fuel pieces and mean
(r = 0.17), and with
(r = 0.15), comparing the hourly peak concentration of
(r= 0.44) and
(r = 0.43) exhibits a moderate correlation with fuel pieces. The scatter plots in Figure 13
and Figure 14
chart the relation between peak hourly levels to the possible co-factors of fuel amount and duration of usage. In Figure 13
a,b, higher concentration peak levels are clustered towards the left of the x-axis. This indicates a non-linear relationship with fuel pieces.
While correlation between fuel pieces and hourly mean concentration is weak, it is stronger when compared to the hourly peak concentration. Therefore, the findings suggest that the peak hourly concentrations are often higher by a minimum of 250% and a maximum of 400% when participants have refuelled their stove more than once during a usage compared to one refuel or none at all. As such, the findings indicate that the ‘flooding’ of indoor space occurs as a result of the stove door being opened for refuelling. This accords with several existing real-world [21
], and lab-based [20
] studies into stoves outside the UK. While the findings point to the opening of the stove door as the origin for indoor PM emissions, further lab-based research is required into how this might relate to duration, timings, and the point in the burn cycle at which the opening occurs.
The hourly peak concentrations explain the shape of the rug plots, as seen in Figure 10
. The shape of the curves exhibit a distinct broad frequency distribution in the lower PM concentration. This indicates that most of the sensor readings are lower during stove use, but there are also smaller spikes towards the right of x-axis, indicating sensor readings that correspond to higher levels of PM pollution. A ’leakage’ would result in a more uniform shape, and, thus, the presence of the smaller spikes cannot be explained. This echoes Salthammer et al.’s findings [35
] and provides further support for the theory of opening doors being the cause of the indoor air pollution seen rather than a leakage, which appears to be more common to open fires than ‘closed’ stoves (see [54
]). The PM fraction gets dispersed quickly throughout the room due to its smaller size, reverting to lower hourly average concentrations.
3.3.2. Hourly Peak Averages Illustrate a Moderate Correlation with Duration of Use
also illustrates a non-linear relationship between the duration of use and mean
(r = 0.017). This is similar to
(r = 0.021), although, again, comparing the hourly peak concentrations of
(r= 0.4) and
(r = 0.38), it exhibits a moderate correlation with the duration of use. The scatter plots in Figure 14
a,b also reflect this, with higher levels of peak values being continuously registered during the stove use.
Longer usage is associated with greater numbers of fuel pieces used. This result supports the explanation for the ’flooding’ phenomenon observed, with higher short-term peak concentrations being seen during longer periods of use, because these periods are sustained by more refueling actions. This accords with [20
], who also found the lighting and refueling aspects of stove management to form the main pollutant-generating phases of operation.