32051 LH Advanced Fieldwork Research
How does surface type influence PM₂.₅ levels across microclimates in Tokyo, and does
sun/shade exposure further modify this effect?
Word Count: 3981
Generative AI tools were not used in any way to create this assignment.
,Introduction
Particulate matter (PM₂.₅) is well documented in its ability to provoke a range of potentially
fatal respiratory and cardiovascular diseases, contributing to public health concerns,
particularly in highly urbanised areas (Huang et al., 2025). Thus, given the implications of
this pollutant, increasing consideration of local microclimatic factors, such as urban form
and sunlight exposure, must be given to understand PM₂.₅ behaviour at finer resolutions
(Liu et al., 2021). Despite this, little research that draws on the impact of a variety of
variables on PM₂.₅ concentrations in a range of microenvironments (Shi et al., 2019)
remains sparse, even less in a Tokyo-specific context.
Therefore, this research aims to address this gap by conducting in situ environmental
sampling in microenvironments ranging from dense urban streetscapes to semi-natural
spaces, offering a contextual, nuanced understanding of PM₂.₅ behaviour at this hyper-
local scale. This will be executed using three secondary research questions that support
the primary research question:
- How does surface type influence PM₂.₅ concentrations across Tokyo’s microclimatic
environments?
- Does sunlight exposure independently affect PM₂.₅ levels, and does it interact with other
variables?
- Do broader environmental covariates (e.g., temperature, windspeed) explain PM₂.₅
variation?
In doing so, this research may support air quality management strategies and ongoing
environmental discourse surrounding urban form and pollution exposure at this scale.
,Literature Review
Literature highlights that microclimates significantly influence the behaviour of PM₂.₅,
particularly in the context of densely populated areas such as Tokyo. A study by Sun et al.
(2023) found that green, shaded “pocket parks” in Tokyo’s Chuo Ward can greatly
influence microclimatic conditions, cooling daytime temperatures by 1.5–2.7 °C compared
to proximal streets. Thus, cooler spaces can influence how PM₂.₅ disperses locally, with
surface type playing a key role in this. Vujovic et al. (2021) state that impermeable
surfaces, such as concrete and asphalt, absorb and retain significantly more heat than
surfaces such as grass, which contributes to the exacerbation of the urban heat island
effect (UHI), trapping and increasing local pollutant concentrations.
Surface albedo can play a role in this, with cooler, high albedo pavements reflecting more
sunlight and thus staying cooler, reducing this UHI effect and, in turn, lowering local
pollutant concentrations (Jandaghian and Akbari, 2018). Despite this, high albedo surfaces
can also reduce the dispersion of PM₂.₅ in microclimates situationally. Models created by
Ulpiani (2021) project that the use of high albedo paving and roofing would moderately
increase ground-level PM₂.₅ concentrations by 0.3 µg/m³ in Los Angeles, due to the lack of
thermal uplift these surfaces foster, in turn reducing the atmospheric mixing of PM₂.₅. With
heat-absorbing surfaces increasing vertical mixing, dispersing pollutants (ibid.), a trade-off
is highlighted between the cumulative and dispersal effects of a given surface. Thus,
particularly when considering the low albedo of many natural surfaces such as grass
(Zhang et al., 2022), viewing surface types purely for their heat-storing properties and this
effect on pollutant concentrations can be one-dimensional.
Interestingly, much of the literature found uses modelling approaches, focusing on broad
land use categories, but fails to measure fine-scale PM₂.₅ variations across different
microenvironments, such as a shaded grass patch compared to a sunny concrete path, in
proximity. Therefore, this research responds to a lack of in situ studies by capturing these
fine-resolution variations, contrasting with broad-scale modelling. Additionally, very few
studies combine the effects of surface type and multiple other interaction variables. Thus,
by controlling for numerous variables in the same study within the context of Tokyo, this
research aims to clarify contradictions in the literature, explaining why findings are mixed.
, Considering the influence of natural surfaces in airborne particulate removal is highly
relevant to this study. Junior, Bueno and da Silva (2022) highlight the benefits of natural
surfaces in urban spaces, finding that the filtration effects of grass lawns in Rio de Janeiro,
another densely populated city, lowered PM₂.₅ concentrations by 33% compared to a
proximal traffic tunnel entrance. This highlights the cleaning effect natural surfaces can
have, acting as natural filters when grass leaves intercept airborne particles, settling on
these leaves and later washing off (ibid.). These are findings that are supported by the
work of Li et al. (2022), who discovered a 9% PM₂.₅ reduction in grass-covered urban
areas, resulting from pollution deposition into ground surfaces. Literature also
demonstrates how green infrastructure can modify local humidity to reduce PM
concentrations.
A study from Jiang et al. (2024) showed how water vapour from evapotranspiration can
reduce airborne PM₂.₅ concentrations on a university campus in China. This
evapotranspiration resulted in elevated microclimatic humidity, causing PM to grow in size
through hygroscopic uptake and settle faster. This consideration could be key in planning
new urban centres, for example. Nevertheless, PM₂.₅ mitigation by natural surfaces may be
spatially limited: CFD modelling from Jin et al. (2024) investigated how changes in green
infrastructure impacted PM₂.₅, finding that short vegetation, such as grass surfaces,
produced little impact on PM₂.₅ concentrations on a scale beyond the vicinity of the green
spaces they were within. However, whilst the aforementioned studies possess increased
validity due to their recency, they may not represent the microclimatic variation of Tokyo.
Seeing as Tokyo-specific microclimatic PM₂.₅ research is rare, this is a literature gap that
this study aims to fill.
Sunlight and shade too hold key roles in modulating PM₂.₅ concentrations. Jiang et al.
(2024) found that illumination (solar flux) correlated negatively with PM₂.₅ concentrations,
caused by solar radiation heating surfaces and the air, diluting near-surface pollutant
concentrations via vertical mixing, with the opposite being true of shaded areas. Solar
radiation also impacts PM₂.₅ chemically: PM can contain organic compounds that degrade
in UV light, forming secondary organic and inorganic aerosols (SOA), converting VOCs to
nitrates and organics, for example (Srivastava et al., 2022). Research by El Mais et al.
(2023) demonstrates this process. It showed how aromatic hydrocarbons oxidise under
sunlight, leading to SOA formation, increasing the mass of PM₂.₅ and enabling it to settle
How does surface type influence PM₂.₅ levels across microclimates in Tokyo, and does
sun/shade exposure further modify this effect?
Word Count: 3981
Generative AI tools were not used in any way to create this assignment.
,Introduction
Particulate matter (PM₂.₅) is well documented in its ability to provoke a range of potentially
fatal respiratory and cardiovascular diseases, contributing to public health concerns,
particularly in highly urbanised areas (Huang et al., 2025). Thus, given the implications of
this pollutant, increasing consideration of local microclimatic factors, such as urban form
and sunlight exposure, must be given to understand PM₂.₅ behaviour at finer resolutions
(Liu et al., 2021). Despite this, little research that draws on the impact of a variety of
variables on PM₂.₅ concentrations in a range of microenvironments (Shi et al., 2019)
remains sparse, even less in a Tokyo-specific context.
Therefore, this research aims to address this gap by conducting in situ environmental
sampling in microenvironments ranging from dense urban streetscapes to semi-natural
spaces, offering a contextual, nuanced understanding of PM₂.₅ behaviour at this hyper-
local scale. This will be executed using three secondary research questions that support
the primary research question:
- How does surface type influence PM₂.₅ concentrations across Tokyo’s microclimatic
environments?
- Does sunlight exposure independently affect PM₂.₅ levels, and does it interact with other
variables?
- Do broader environmental covariates (e.g., temperature, windspeed) explain PM₂.₅
variation?
In doing so, this research may support air quality management strategies and ongoing
environmental discourse surrounding urban form and pollution exposure at this scale.
,Literature Review
Literature highlights that microclimates significantly influence the behaviour of PM₂.₅,
particularly in the context of densely populated areas such as Tokyo. A study by Sun et al.
(2023) found that green, shaded “pocket parks” in Tokyo’s Chuo Ward can greatly
influence microclimatic conditions, cooling daytime temperatures by 1.5–2.7 °C compared
to proximal streets. Thus, cooler spaces can influence how PM₂.₅ disperses locally, with
surface type playing a key role in this. Vujovic et al. (2021) state that impermeable
surfaces, such as concrete and asphalt, absorb and retain significantly more heat than
surfaces such as grass, which contributes to the exacerbation of the urban heat island
effect (UHI), trapping and increasing local pollutant concentrations.
Surface albedo can play a role in this, with cooler, high albedo pavements reflecting more
sunlight and thus staying cooler, reducing this UHI effect and, in turn, lowering local
pollutant concentrations (Jandaghian and Akbari, 2018). Despite this, high albedo surfaces
can also reduce the dispersion of PM₂.₅ in microclimates situationally. Models created by
Ulpiani (2021) project that the use of high albedo paving and roofing would moderately
increase ground-level PM₂.₅ concentrations by 0.3 µg/m³ in Los Angeles, due to the lack of
thermal uplift these surfaces foster, in turn reducing the atmospheric mixing of PM₂.₅. With
heat-absorbing surfaces increasing vertical mixing, dispersing pollutants (ibid.), a trade-off
is highlighted between the cumulative and dispersal effects of a given surface. Thus,
particularly when considering the low albedo of many natural surfaces such as grass
(Zhang et al., 2022), viewing surface types purely for their heat-storing properties and this
effect on pollutant concentrations can be one-dimensional.
Interestingly, much of the literature found uses modelling approaches, focusing on broad
land use categories, but fails to measure fine-scale PM₂.₅ variations across different
microenvironments, such as a shaded grass patch compared to a sunny concrete path, in
proximity. Therefore, this research responds to a lack of in situ studies by capturing these
fine-resolution variations, contrasting with broad-scale modelling. Additionally, very few
studies combine the effects of surface type and multiple other interaction variables. Thus,
by controlling for numerous variables in the same study within the context of Tokyo, this
research aims to clarify contradictions in the literature, explaining why findings are mixed.
, Considering the influence of natural surfaces in airborne particulate removal is highly
relevant to this study. Junior, Bueno and da Silva (2022) highlight the benefits of natural
surfaces in urban spaces, finding that the filtration effects of grass lawns in Rio de Janeiro,
another densely populated city, lowered PM₂.₅ concentrations by 33% compared to a
proximal traffic tunnel entrance. This highlights the cleaning effect natural surfaces can
have, acting as natural filters when grass leaves intercept airborne particles, settling on
these leaves and later washing off (ibid.). These are findings that are supported by the
work of Li et al. (2022), who discovered a 9% PM₂.₅ reduction in grass-covered urban
areas, resulting from pollution deposition into ground surfaces. Literature also
demonstrates how green infrastructure can modify local humidity to reduce PM
concentrations.
A study from Jiang et al. (2024) showed how water vapour from evapotranspiration can
reduce airborne PM₂.₅ concentrations on a university campus in China. This
evapotranspiration resulted in elevated microclimatic humidity, causing PM to grow in size
through hygroscopic uptake and settle faster. This consideration could be key in planning
new urban centres, for example. Nevertheless, PM₂.₅ mitigation by natural surfaces may be
spatially limited: CFD modelling from Jin et al. (2024) investigated how changes in green
infrastructure impacted PM₂.₅, finding that short vegetation, such as grass surfaces,
produced little impact on PM₂.₅ concentrations on a scale beyond the vicinity of the green
spaces they were within. However, whilst the aforementioned studies possess increased
validity due to their recency, they may not represent the microclimatic variation of Tokyo.
Seeing as Tokyo-specific microclimatic PM₂.₅ research is rare, this is a literature gap that
this study aims to fill.
Sunlight and shade too hold key roles in modulating PM₂.₅ concentrations. Jiang et al.
(2024) found that illumination (solar flux) correlated negatively with PM₂.₅ concentrations,
caused by solar radiation heating surfaces and the air, diluting near-surface pollutant
concentrations via vertical mixing, with the opposite being true of shaded areas. Solar
radiation also impacts PM₂.₅ chemically: PM can contain organic compounds that degrade
in UV light, forming secondary organic and inorganic aerosols (SOA), converting VOCs to
nitrates and organics, for example (Srivastava et al., 2022). Research by El Mais et al.
(2023) demonstrates this process. It showed how aromatic hydrocarbons oxidise under
sunlight, leading to SOA formation, increasing the mass of PM₂.₅ and enabling it to settle
