*2.4. Indoor PM2.5*

The indoor fugitive emission and infiltration of outdoor pollution are the two major sources of household pollutants (please see the schematic in Figure 3). Such external infiltration from the upstream community is determined by ventilation type, room volume, flow direction, and nature and size of openings in walls, windows, and doors. The ventilation in rural households is usually natural, wherein the leakage of airflow is through the openings in the building walls, windows, and doors. The dimensionless infiltration factor of outdoor pollution to the indoors is described by [14,32]:

$$F\_{inf} = \frac{P \ast a}{a + k} \tag{1}$$

where *a* is the air changes per hour (ACH), *P* is the penetration coefficient that indicates the fraction of outdoor pollutant passing indoors [33], and *k* is the pollutant deposition rate per hour. ACH is the rate of indoor air replacement by outdoor air. It is an important parameter that determines air ventilation in microenvironments, thus affecting indoor air exposure [34].When particles penetrate through the building envelope, gravity, diffusion, and inertial interaction are the major determinants of *P*. *P* shows a hill-shape distribution with respect to particle size, and it is assumed to be 0.8 in the study case [32]. *K* describes the flux of particulate matter deposited on the frames of windows, doors, walls, and other surfaces when traveling indoors and it is adapted to be 0.53 h−<sup>1</sup> for particle size at 2.5 µm, based on experiments in six naturally ventilated houses [32]. *Sustainability* **2023**, *15*, x FOR PEER REVIEW 5 of 14

**Figure 3.** Household pollution sources from indoor fugitive emission and outdoor infiltration*.* **Figure 3.** Household pollution sources from indoor fugitive emission and outdoor infiltration.

The ACH in a region can vary substantially based on the local wind speed, location, and the closing/opening of the door and windows. Figure 4 shows different ACH meas-The ACH in a region can vary substantially based on the local wind speed, location, and the closing/opening of the door and windows. Figure 4 shows different ACH measured

ured in rural areas in India [7], Bangladesh [34], China [35], and Nepal [36]. When the windows are closed, the ventilation is solely dependent on the leakage through gaps. The

WHO suggested a default ACH of 21, for kitchens using biomass for cooking, to achieve the indoor air quality standard [37]. For current analysis, indoor pollution concentration

Using the calculated dimensionless infiltration factor, the outdoor infiltration is then

= × (2)

, which is obtained from the disper-

and residents' potential dose were estimated for ACH range of 1–25.

**Figure 4.** Reference rural air changes per hour in different regions*.*

where *Cout* is the outdoor PM2.5 concentration in μg/m<sup>3</sup>

described by:

sion model output.

in rural areas in India [7], Bangladesh [34], China [35], and Nepal [36]. When the windows are closed, the ventilation is solely dependent on the leakage through gaps. The Literature states that the ACH in such conditions is around 1–2 for houses with solid walls. WHO suggested a default ACH of 21, for kitchens using biomass for cooking, to achieve the indoor air quality standard [37]. For current analysis, indoor pollution concentration and residents' potential dose were estimated for ACH range of 1–25. ured in rural areas in India [7], Bangladesh [34], China [35], and Nepal [36]. When the windows are closed, the ventilation is solely dependent on the leakage through gaps. The Literature states that the ACH in such conditions is around 1–2 for houses with solid walls. WHO suggested a default ACH of 21, for kitchens using biomass for cooking, to achieve the indoor air quality standard [37]. For current analysis, indoor pollution concentration and residents' potential dose were estimated for ACH range of 1–25.

The ACH in a region can vary substantially based on the local wind speed, location, and the closing/opening of the door and windows. Figure 4 shows different ACH meas-

**Figure 3.** Household pollution sources from indoor fugitive emission and outdoor infiltration*.*

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 5 of 14

Using the calculated dimensionless infiltration factor, the outdoor infiltration is then described by: Using the calculated dimensionless infiltration factor, the outdoor infiltration is then described by:

$$\mathbf{C}\_b = F\_{\inf} \times \mathbf{C}\_{out} \tag{2}$$

 = × (2) where *Cout* is the outdoor PM2.5 concentration in μg/m<sup>3</sup> , which is obtained from the disperwhere *Cout* is the outdoor PM2.5 concentration in µg/m<sup>3</sup> , which is obtained from the dispersion model output.

sion model output. Computational Fluid Dynamic (CFD) models are at times utilized for indoor air quality modeling [38,39]. In contrast, the current study deployed a computationally inexpensive single zone mass balance model [40] to estimate the indoor generated PM2.5 concentration, which is described by:

$$\mathcal{C}(t) = \mathcal{C}\_b + \frac{\mathcal{S}}{V(a+k)} + (\mathcal{C}\_{ini} + \mathcal{C}\_b + \frac{\mathcal{S}}{V(a+k)})e^{-(a+k)t} \tag{3}$$

where *V* is the kitchen volume, which is ~40 m<sup>3</sup> for multiple regions [41]. *S* is the indoor source emission rate in µg/h. In reality, the emission rate measurements vary widely, based on factors such as experimental methodology, combustion facilities, and fuel properties [8]. The indoor fugitive emission is around 2–5% of the total emission, based on the laboratory experiments, as well as field measurements [8,10,41]. Household energy needs are met mainly by biomass fuels, including crop residues, wood, coal, etc. Usage of different types of fuels impact the overall emission levels. Figure 5 presents the PM2.5 emission factor (EF) of different fuel types, in the laboratory [42] and in field, in different regions including China [8], Nepal [42], Mexico [43], and India [42]. For the same type of fuel, the EF differences may be due to factors such as fuel shape, moisture content, and the stove differences. With the different emission factors, one can estimate the emission inventory based on the fuel consumption rate from the various cooking events [44].

based on the fuel consumption rate from the various cooking events [44].

Computational Fluid Dynamic (CFD) models are at times utilized for indoor air quality modeling [38,39]. In contrast, the current study deployed a computationally inexpensive single zone mass balance model [40] to estimate the indoor generated PM2.5 concen-

> ( + )

)

−(+)

(3)

+ ( + +

where *V* is the kitchen volume, which is ~40 m3 for multiple regions [41]. *S* is the indoor source emission rate in μg/h. In reality, the emission rate measurements vary widely, based on factors such as experimental methodology, combustion facilities, and fuel properties [8]. The indoor fugitive emission is around 2–5% of the total emission, based on the laboratory experiments, as well as field measurements [8,10,41]. Household energy needs are met mainly by biomass fuels, including crop residues, wood, coal, etc. Usage of different types of fuels impact the overall emission levels. Figure 5 presents the PM2.5 emission factor (EF) of different fuel types, in the laboratory [42] and in field, in different regions including China [8], Nepal [42], Mexico [43], and India [42]. For the same type of fuel, the EF differences may be due to factors such as fuel shape, moisture content, and the stove differences. With the different emission factors, one can estimate the emission inventory

**Figure 5.** Reference emission factor for different fuel types*.* **Figure 5.** Reference emission factor for different fuel types.

where *C<sup>A</sup>* is the concentration of PM2.5 in μg/m<sup>3</sup>

#### *2.5. Health Risk Assessment 2.5. Health Risk Assessment*

tration, which is described by:

() = +

 ( + )

The potential dose of stove-induced PM2.5 for individuals under long-term exposure to cooking emissions is assessed using US EPA risk assessment [20]. The potential dose (*I*) in μg/kg·day of PM2.5 can be quantified as [15,45,46]: The potential dose of stove-induced PM2.5 for individuals under long-term exposure to cooking emissions is assessed using US EPA risk assessment [20]. The potential dose (*I*) in µg/kg·day of PM2.5 can be quantified as [15,45,46]:

$$I = C\_A \frac{IR \times ET \times EF \times ED}{BW} \times \frac{1}{AT} \tag{4}$$

. *ET* is the exposure time in h/day, which

in this study, is assumed to be 3 h exposure per day [47]. *EF* is the exposure frequency (days/year). *ED* is the exposure duration in the study period. *AT* is the average time of exposure in a day, which is *ED* × 365. *BW* is the body weight (kg). *IR* is the inhalation rate (m<sup>3</sup> /h), which represents the volume of air inhaled over a specified timeframe. The inhalation rates are typically indexed to activity levels. The inhalation rate for different age groups, segregated by gender and the average body weight, is referred from the EPA Exposure Factors Handbook [48]. Note that for the male gender, the potential dose results are very similar, within 2–7%. For brevity, here we present only results calculated using available female parameters, as in many rural locations, the primary coking activities are carried out by women. Under moderate activity levels, for age groups from 0.5–3, 3–10, where *C<sup>A</sup>* is the concentration of PM2.5 in µg/m<sup>3</sup> . *ET* is the exposure time in h/day, which in this study, is assumed to be 3 h exposure per day [47]. *EF* is the exposure frequency (days/year). *ED* is the exposure duration in the study period. *AT* is the average time of exposure in a day, which is *ED* × 365. *BW* is the body weight (kg). *IR* is the inhalation rate (m3/h), which represents the volume of air inhaled over a specified timeframe. The inhalation rates are typically indexed to activity levels. The inhalation rate for different age groups, segregated by gender and the average body weight, is referred from the EPA Exposure Factors Handbook [48]. Note that for the male gender, the potential dose results are very similar, within 2–7%. For brevity, here we present only results calculated using available female parameters, as in many rural locations, the primary coking activities are carried out by women. Under moderate activity levels, for age groups from 0.5–3, 3–10, and 10–18 years old, the average body weight is 11 kg, 23 kg, and 50 kg, and the average inhalation rate is 0.6 m3/h, 0.9 m3/h, and 1.26 m3/h, respectively, while for the adult age groups from 18–30, 30–60, and above 60 years old, the average body weight is 62 kg, 68 kg, and 67 kg, and the average inhalation rate is 1.32 m3/h, 1.32 m3/h, and 1.2 m3/h, respectively.

The risk quotient is often used to inform the health implications due to pollutant exposure. The risk quotient is described by:

$$RQ = \frac{I}{RfD} \tag{5}$$

where *RfD* is the reference dose of PM2.5 (µg/kg·day) and represents the safe average daily dose. *RfD* is calculated from Equation (4), with a reference concentration of 5 µg/m<sup>3</sup> . If RQ < 1, the exposure is not considered adverse to public health; if RQ > 1, the exposure is considered detrimental to public health.

#### **3. Results 3. Results**

tively.

#### *3.1. Outdoor Pollution Level 3.1. Outdoor Pollution Level*

exposure. The risk quotient is described by:

considered detrimental to public health.

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 7 of 14

and 10–18 years old, the average body weight is 11 kg, 23 kg, and 50 kg, and the average inhalation rate is 0.6 m3/h, 0.9 m3/h, and 1.26 m3/h, respectively, while for the adult age groups from 18–30, 30–60, and above 60 years old, the average body weight is 62 kg, 68 kg, and 67 kg, and the average inhalation rate is 1.32 m3/h, 1.32 m3/h, and 1.2 m3/h, respec-

The risk quotient is often used to inform the health implications due to pollutant

(5)

. If RQ

where *RfD* is the reference dose of PM2.5 (μg/kg·day) and represents the safe average daily dose. *RfD* is calculated from Equation (4), with a reference concentration of 5 μg/m<sup>3</sup>

< 1, the exposure is not considered adverse to public health; if RQ > 1, the exposure is

=

A case study of neighborhood pollution attributed to chimney vented PM2.5 emissions is conducted using QUIC model, with a wind speed of 2 m/s (at 10 m height) and a south-southwest wind direction. The QUIC modeling results of ambient PM2.5 pollution during the steady cooking state is shown in Figure 6. The channeling effect caused by wind flow encountering the building obstacles will lead to accumulation of PM2.5 in the building wakes [23]. The QUIC model enables the detection of severely impacted regions, considering the building morphology. A typical rural village consists of regions with different building densities. In each of these building densities, the neighborhood pollution level varies because the effects of trapping PM2.5 near buildings differ. As indicated in Figure 6, the maximum ground level concentration occurred in the high building density region. Although the prevailing wind is from the south-southwest, the buildings in the highdensity region substantially disturb the flow and consequently, distribute the emissions in different directions, while in the low-density region, the rarely disturbed flow quickly dilutes PM2.5. A case study of neighborhood pollution attributed to chimney vented PM*2.5* emissions is conducted using QUIC model, with a wind speed of 2 m/s (at 10 m height) and a southsouthwest wind direction. The QUIC modeling results of ambient PM*2.5* pollution during the steady cooking state is shown in Figure 6. The channeling effect caused by wind flow encountering the building obstacles will lead to accumulation of PM2.5 in the building wakes [23]. The QUIC model enables the detection of severely impacted regions, considering the building morphology. A typical rural village consists of regions with different building densities*.* In each of these building densities, the neighborhood pollution level varies because the effects of trapping PM2.5 near buildings differ. As indicated in Figure 6, the maximum ground level concentration occurred in the high building density region. Although the prevailing wind is from the south-southwest, the buildings in the high-density region substantially disturb the flow and consequently, distribute the emissions in different directions, while in the low-density region, the rarely disturbed flow quickly dilutes PM2.5.

**Figure 6.** Outdoor dispersion modeling results: (**a**,**c**) flow trajectory for a low- and high-density region, respectively; (**b**,**d**). PM2.5 mass concentration distribution in a low- and high-density region, respectively.

The current analysis used an emission rate of 52 mg/min for the outdoor pollution modeling based on field measurements, with 96% of total emissions from chimneys [41]. The mean PM2.5 in the high building density region is 21.2 <sup>±</sup> 4.26 <sup>µ</sup>g/m<sup>3</sup> . The mean PM2.5 in the low building density region is 4.57 <sup>±</sup> 2.8 <sup>µ</sup>g/m<sup>3</sup> . The high-density area is more impacted, regardless of the wind direction, due to the building density. Hence, this region has the highest level of PM2.5 in the communities.

Other factors, such as seasonal relative humidity, temperature, and precipitation, also play a significant role in outdoor pollution dispersion across seasons [49]. In the Brazilian rainforest, during the dry season, exposures to PM2.5 can be 6 times higher than during the rainy season [15]. This lower exposure can be attributed to the leaching of air pollution to the ground as a result of higher precipitation in the rainy season [50].

#### *3.2. Indoor Pollution Level* pollution and exposure, such as increasing the natural ventilation rates [51]. Combining

*3.2. Indoor Pollution Level*

respectively.

Instead of completely switching to clean fuel, which may be impractical, many studies have also recommended alternative actions to reduce household air pollution and exposure. Other studies have also recommended alternative actions to reduce household air pollution and exposure, such as increasing the natural ventilation rates [51]. Combining chimneys with improved combustion chambers in stoves can also result in substantially reduced overall emissions, although chimney maintenance is necessary to maintain these reductions [41]. Leakage emissions from well-maintained stoves were shown to be substantially lower than from those in need of repair, such as the filling of cracks and the cleaning of chimneys [52]. chimneys with improved combustion chambers in stoves can also result in substantially reduced overall emissions, although chimney maintenance is necessary to maintain these reductions [41]. Leakage emissions from well-maintained stoves were shown to be substantially lower than from those in need of repair, such as the filling of cracks and the cleaning of chimneys [52]. Despite the up to 90% reductions in indoor air concentrations of PM2.5 associated with the installation of chimneys [53], fugitive concentration, combined with outdoor infiltrations, still contributes to poor indoor air quality. The indoor pollutant concentration is

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 14

the low building density region is 4.57 ± 2.8 μg/m<sup>3</sup>

has the highest level of PM2.5 in the communities.

The mean PM2.5 in the high building density region is 21.2 ± 4.26 μg/m<sup>3</sup>

**Figure 6.** Outdoor dispersion modeling results: (**a**,**c**) flow trajectory for a low- and high-density region, respectively; (**b**,**d**). PM2.5 mass concentration distribution in a low- and high-density region,

The current analysis used an emission rate of 52 mg/min for the outdoor pollution modeling based on field measurements, with 96% of total emissions from chimneys [41].

pacted, regardless of the wind direction, due to the building density. Hence, this region

also play a significant role in outdoor pollution dispersion across seasons [49]. In the Brazilian rainforest, during the dry season, exposures to PM2.5 can be 6 times higher than during the rainy season [15]. This lower exposure can be attributed to the leaching of air pol-

lution to the ground as a result of higher precipitation in the rainy season [50].

Other factors, such as seasonal relative humidity, temperature, and precipitation,

Instead of completely switching to clean fuel, which may be impractical, many stud-

ies have also recommended alternative actions to reduce household air pollution and exposure. Other studies have also recommended alternative actions to reduce household air

. The mean PM2.5 in

. The high-density area is more im-

Despite the up to 90% reductions in indoor air concentrations of PM2.5 associated with the installation of chimneys [53], fugitive concentration, combined with outdoor infiltrations, still contributes to poor indoor air quality. The indoor pollutant concentration is determined by the fugitive emission rates, room volume, particulate matter decay rate, and the infiltration of pollution from outdoors due to ventilation. Figure 7 shows the modeled indoor generated PM2.5 mass concentrations during 1 h of cooking under different air exchange rates, incorporating contributions of both fugitive emissions and neighborhood infiltration for high-packing density. The fugitive emission rate ranges from 0.26–2.6 mg/min, based on direct field measurements in different regions [8,10,41,54]. The ACH ranges from 1–25 h−<sup>1</sup> , from poor ventilated cases, to WHO default ventilation rates for ISO tiers. determined by the fugitive emission rates, room volume, particulate matter decay rate, and the infiltration of pollution from outdoors due to ventilation. Figure 7 shows the modeled indoor generated PM2.5 mass concentrations during 1 h of cooking under different air exchange rates, incorporating contributions of both fugitive emissions and neighborhood infiltration for high-packing density. The fugitive emission rate ranges from 0.26–2.6 mg/min, based on direct field measurements in different regions [8,10,41,54]. The ACH ranges from 1–25 h−1, from poor ventilated cases, to WHO default ventilation rates for ISO tiers.

**Figure 7.** Indoor PM2.5 concentrations under different ACH incorporating fugitive emission and neighborhood infiltration for a high-packing density neighborhood.

Infiltration of pollution from outdoors consists of both neighborhood pollution and regional background PM2.5. The neighborhood pollution contribution to indoor concentrations depends on the packing density of upstream homes. Figure 8 shows the percentage of fugitive contribution to the total indoor air PM2.5 concentrations. At 25 ACH, for homes in the high packing density area, the indoor generated PM2.5 accounts for 90% of the total concentration, while for a low packing density region, the number is 98%.

concentration, while for a low packing density region, the number is 98%.

concentration, while for a low packing density region, the number is 98%.

neighborhood infiltration for a high-packing density neighborhood.

neighborhood infiltration for a high-packing density neighborhood.

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 9 of 14

**Figure 8.** Contribution of fugitive emission to indoor air PM2.5 concentrations. **Figure 8.** Contribution of fugitive emission to indoor air PM2.5 concentrations. After the cooking concludes, the decay trend of the relative mass concentration

After the cooking concludes, the decay trend of the relative mass concentration (C(t)/Cmax) under ACH = 1, 5, 10, 20, and 25 are shown in Figure 9. *S*tudies indicated that the building characteristics, including ventilation, orientation, the morphology of the streets, wall construction, eave spaces, open–closed windows, etc., dominate the air exchange rate [50,55]. After the cooking concludes, the decay trend of the relative mass concentration (C(t)/Cmax) under ACH = 1, 5, 10, 20, and 25 are shown in Figure 9. Studies indicated that the building characteristics, including ventilation, orientation, the morphology of the streets, wall construction, eave spaces, open–closed windows, etc., dominate the air exchange rate [50,55]. (C(t)/Cmax) under ACH = 1, 5, 10, 20, and 25 are shown in Figure 9. *S*tudies indicated that the building characteristics, including ventilation, orientation, the morphology of the streets, wall construction, eave spaces, open–closed windows, etc., dominate the air exchange rate [50,55].

**Figure 7.** Indoor PM2.5 concentrations under different ACH incorporating fugitive emission and

**Figure 7.** Indoor PM2.5 concentrations under different ACH incorporating fugitive emission and

Infiltration of pollution from outdoors consists of both neighborhood pollution and regional background PM2.5.The neighborhood pollution contribution to indoor concentrations depends on the packing density of upstream homes. Figure 8 shows the percentage of fugitive contribution to the total indoor air PM*2.5* concentrations. At 25 ACH, for homes in the high packing density area, the indoor generated PM*2.5* accounts for 90% of the total

Infiltration of pollution from outdoors consists of both neighborhood pollution and regional background PM2.5.The neighborhood pollution contribution to indoor concentrations depends on the packing density of upstream homes. Figure 8 shows the percentage of fugitive contribution to the total indoor air PM*2.5* concentrations. At 25 ACH, for homes in the high packing density area, the indoor generated PM*2.5* accounts for 90% of the total

**Figure 9.** Influence of different ACHs on relative indoor concentration decay trend after steady cooking events. **Figure 9.** Influence of different ACHs on relative indoor concentration decay trend after steady cooking events. **Figure 9.** Influence of different ACHs on relative indoor concentration decay trend after steady cooking events.

This case study uses a fugitive emission rate of 2.1 mg/min, which is directly measured in rural Mexico using nested hoods to capture all emissions [41]. Even at the recommended 21 ACH, the indoor pollution concentration during the 1 h steady cooking event is 174 µg/m<sup>3</sup> , with fugitive emissions contributing 90% to indoor concentrations. The corresponding 24 h average PM2.5 level is ~21 µg/m<sup>3</sup> under the assumption that each household conducts 3 h cooking each day [47]. This exceeds the 2021 WHO air quality guideline of 15 µg/m<sup>3</sup> for 24 h.

PM2.5 concentration levels in different rooms can be significantly lower than in other rooms [56] and can vary by season [49]. Zuk et al. found that the kitchen concentrations

were two times that of other rooms [56]. In addition, in many homes cooking-generated PM2.5 may readily spread to the adjacent rooms in the house [49,57]. Since people spend the most time in bedrooms and living rooms, having a separate kitchen can help reduce exposures, although, room ventilation and location relative to the kitchen have been reported to impact the PM2.5 level in the room [58]. Behavior changes, such as opening doors and windows [59] and the use of extraction fans, may also reduce indoor concentrations. PM2.5 may readily spread to the adjacent rooms in the house [49,57]. Since people spend the most time in bedrooms and living rooms, having a separate kitchen can help reduce exposures, although, room ventilation and location relative to the kitchen have been reported to impact the PM2.5 level in the room [58]. Behavior changes, such as opening doors and windows [59] and the use of extraction fans, may also reduce indoor concentrations.

This case study uses a fugitive emission rate of 2.1 mg/min, which is directly measured in rural Mexico using nested hoods to capture all emissions [41]. Even at the recommended 21 ACH, the indoor pollution concentration during the 1 h steady cooking event

PM2.5 concentration levels in different rooms can be significantly lower than in other rooms [56] and can vary by season [49]. Zuk et al. found that the kitchen concentrations were two times that of other rooms [56]. In addition, in many homes cooking-generated

responding 24 h average PM2.5 level is ~21 μg/m<sup>3</sup> under the assumption that each household conducts 3 h cooking each day [47]. This exceeds the 2021 WHO air quality guideline

, with fugitive emissions contributing 90% to indoor concentrations. The cor-

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 10 of 14

#### *3.3. Potential Dose Assessment 3.3. Potential Dose Assessment* The inhaled dose is determined by individual behaviors and the distance from the

for 24 h.

is 174 μg/m<sup>3</sup>

of 15 μg/m<sup>3</sup>

The inhaled dose is determined by individual behaviors and the distance from the emission source. A number of studies have shown that personal exposures are ~50% of indoor kitchen concentrations [52,60], as personal exposures include time spent away from the kitchen in other environments. Figure 10 shows the potential dose estimated under personal exposure. The risk quotient (RQ) for exposed residents is 26, 7.93, 3.96, and 2.19 under 1, 5, 10, and 20 ACH, respectively. This shows that even under high ventilation rates, household emissions moderately contribute to total chronic exposure and may induce respiratory health problems. Besides, the potential intake of pollutants from cooking activity is high, and thus, the long-term exposure can significantly impact individuals who perform the cooking. emission source. A number of studies have shown that personal exposures are ~50% of indoor kitchen concentrations [52,60], as personal exposures include time spent away from the kitchen in other environments. Figure 10 shows the potential dose estimated under personal exposure. The risk quotient (RQ) for exposed residents is 26, 7.93, 3.96, and 2.19 under 1, 5, 10 ,and 20 ACH, respectively. This shows that even under high ventilation rates, household emissions moderately contribute to total chronic exposure and may induce respiratory health problems. Besides, the potential intake of pollutants from cooking activity is high, and thus, the long-term exposure can significantly impact individuals who perform the cooking.

**Figure 10.** Case study of potential dose of PM2.5 for different age groups under different ventilation parameters. **Figure 10.** Case study of potential dose of PM2.5 for different age groups under different ventilation parameters.

In general, potential PM*2.5* doses decreased with age groups, and children under 3 years had the highest potential dose, in agreement with other studies [15,50], because In general, potential PM2.5 doses decreased with age groups, and children under 3 years had the highest potential dose, in agreement with other studies [15,50], because younger children are more active and breathe more per unit of body weight.

younger children are more active and breathe more per unit of body weight. A study of chimney stove impact conducted over a period of 12 months by Chakraborty et al. found that the median value of PM2.5 RQ was 1.63 [61]. Although the utilization of a chimney stove has adverse health effects, the results show a lower PM2.5 A study of chimney stove impact conducted over a period of 12 months by Chakraborty et al. found that the median value of PM2.5 RQ was 1.63 [61]. Although the utilization of a chimney stove has adverse health effects, the results show a lower PM2.5 potential dose compared to that from a traditional open fire, for which the observed RQ can be as high as 5.57 [61].

Single-zone models tend to overestimate concentrations, as the model assumes a well-mixed environment, which may attribute to the discrepancies in the calculated and measured concentration [62]. The overestimation of potential dose can thus be a limitation of this approach.

#### **4. Conclusions**

The aim of this study is to provide a full-cycle analysis integrating air quality models, infiltration models, and risk assessment models to better understand the impacts of

neighborhood pollution and stove fugitive emissions on the potential dose. The main conclusions of this study are:


**Supplementary Materials:** The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/su15075676/s1, Figure S1: Comparison of meteorological approximation model output and field measurement; Figure S2: Comparison of point source dispersion among Gaussian, QUIC, and water channel evaluation; Figure S3: Comparison of (left) AERMOD output with (right) QUIC output for one-hour outdoor emission estimation. References [18,19,63–65] are cited in the supplementary materials.

**Author Contributions:** Conceptualization, Y.H., S.R.N., R.D.E. and M.P.; data curation, Y.H. and S.R.N.; formal analysis, Y.H. and S.R.N.; investigation, Y.H. and S.R.N.; software, Y.H. and S.R.N.; validation, Y.H. and S.R.N.; visualization, Y.H. and S.R.N.; methodology, Y.H. and S.R.N.; funding acquisition, R.D.E.; project administration, R.D.E. and M.P.; supervision, M.P. and R.D.E.; writing—original draft, Y.H. and S.R.N.; writing—review and editing, Y.H., S.R.N., R.D.E. and M.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by Clean Stacking Options and Regional IAP Scenarios for Rural Mexico, NIH-5585744.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We are grateful to the Los Alamos National Laboratory for providing the QUIC model under license number: LIC-20-04147. Special thanks to Hannah Lee, Rebecca Albano, and Arthor Bernal for running the QUIC simulation.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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