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> This paper discusses how changes in the environment due to indust...

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During his stays in London between the autumn of 1899 and the early...

> "Furthermore, Turner and Monet’s works span the Industrial Revolu...

Aerosols are tiny particles or droplets suspended in the atmosphere...

It is important to benchmark the wavelet method with photographs wh...

This is extreme. 20th/21st century emissions have also been heavily...

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> “It is clear that industrialization changed the environmental con...

> "Our basic premise is that Impressionism—as developed in the work...

RESEARCH ARTICLE APPLIED PHYSICAL SCIENCES

OPEN ACCESS

Paintings by Turner and Monet depict trends in 19th century

air pollution

Anna Lea Albright

a,1

and Peter Huybers

Edited by William Clark, Harvard University, Cambridge, MA; received November 8, 2022; accepted December 20, 2022

Individual paintings by artists including Vincent van Gogh and Edvard Munch

have been shown to depict speciﬁc atmospheric phenomena, raising the question

of whether longer-term environmental change inﬂuences stylistic trends in painting.

Anthropogenic aerosol emissions increased to unprecedented levels during the 19th

century as a consequence of the Industrial Revolution, particularly in Western European

cities, leading to an optical environment having less contrast and more intensity.

Here, we show that trends from more ﬁgurative to impressionistic representations in

J.M.W. Turner and Claude Monet’s paintings in London and Paris over the 19th

century accurately render physical changes in their local optical environment. In

particular, we demonstrate that changes in local sulfur dioxide emissions are a highly

statistically signiﬁcant explanatory variable for trends in the contrast and intensity of

Turner, Monet, and others’ works, including after controlling for time trends and

subject matter. Industrialization altered the environmental context in which Turner

and Monet painted, and our results indicate that their paintings capture changes in

the optical environment associated with increasingly polluted atmospheres during the

Industrial Revolution.

air pollution | artwork | environmental reconstruction | atmospheric science

Some works of art, even those that do not appear “realistic,” appear to faithfully record

particular natural phenomena. Edvard Munch’s The Scream (1893), for example, is

argued to depict nacreous clouds (1). Vincent van Gogh’s Moonrise (1889) is dated to

precisely 9:08 p.m. local time on July 13, 1889, using topographic observations, lunar

tables, and letters (2). Nine of Claude Monet’s paintings in his London series are also

dated using solar geometry, with results conﬁrmed by cross-referencing against Monet’s

letters (3). A survey of over 12,000 paintings, moreover, indicates that different schools

reﬂect local meteorological conditions, such as paler blue skies in the British school than

other contemporaneous European schools (4). Another important example of paintings

depicting the natural environment comes from a set of studies of sunset coloration over

time relative to volcanic eruptions that injected aerosols into the stratosphere (5, 6).

Sunsets seen through an aerosol-laden stratosphere appear redder because of greater

scattering in the limb of the Earth’s atmosphere (7). Across schools of painting, the

red-to-green ratios in sunset paintings from 1500 to 1900 are correlated with independent

proxies of stratospheric aerosol content (5, 6), though difﬁculty constraining the aerosol

size distribution and solar zenith angle introduces uncertainties to this methodology (8).

Here, we seek to ascertain whether there is a relationship between changes in

atmospheric conditions associated with industrialization and changes in painting style—

primarily that of the British artist Joseph Mallord William Turner (1775 to 1851) and

French artist Claude Monet (1840 to 1926). We focus on Turner and Monet because they

proliﬁcally painted landscapes and cityscapes, often with repeated motifs. Furthermore,

Turner and Monet’s works span the Industrial Revolutions starting in Great Britain in

the late 18th century, a time of unprecedented growth in air pollution (9–11). Over

the course of their careers, Turner and Monet’s painting styles change from sharper to

hazier contours and toward a whiter palette, a progression that is typically characterized

as moving from a more ﬁgurative to impressionistic style. We explore the hypothesis

that increasingly impressionistic paintings by Turner, Monet, and several other artists

represent, at least in part, physical changes in atmospheric optical conditions.

Optical Implications of Increasing Aerosol Concentrations

As illustrated in Fig. 1, aerosols absorb and scatter radiation both into and out of a

line of sight. This scattering tends to decrease the contrast between otherwise distinct

objects (12, 13). Edges are used to quantify contrast because they often show the intensity

Signiﬁcance

Individual paintings are known to

depict snapshots of particular

atmospheric phenomena, raising

the possibility that paintings

could also document longer-term

environmental change.

During the Industrial Revolution,

air pollution increased to

unprecedented levels, but these

values remain uncertain given the

lack of widespread, direct

measurements. Here, we show

that stylistic changes from more

figurative to impressionistic

paintings by Turner and Monet

over the 19th century strongly

covary with increasing levels of air

pollution. In particular, stylistic

changes in their work toward

hazier contours and a whiter

color palette are consistent with

the optical changes expected

from higher atmospheric aerosol

concentrations. These results

indicate that Turner and Monet’s

paintings capture elements of the

atmospheric environmental

transformation during the

Industrial Revolution.

Author contributions: A.L.A. and P.H. designed research;

performed research; contributed new analytic tools;

analyzed data; and wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

This open access article is distributed under Creative

Commons Attribution License 4.0 (CC BY).

To whom correspondence may be addressed. Email:

annalea.albright@gmail.com.

This article contains supporting information online

at http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.

2219118120/-/DCSupplemental.

Published January 31, 2023.

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absorption

ection

in-scattering

out-scattering

Fig. 1. Schematic illustrating key processes by which aerosols influence an object’s contrast, intensity, and visibility. A theoretical object (denoted by the gray

disk) reflecting light (black arrows) is visible because of its contrast with the background light (pale blue arrow). Aerosols (navy dots) in the air column scatter

background light into the line of vision (“in-scattering,” highlighted in light yellow), scatter object light out of the line of vision (“out-scattering”), and absorb light.

These optical eﬀects from aerosols lead a viewer to perceive an object as having less-distinct edges (less contrast) and a whiter tint (increased intensity), as

idealized by the images on the left- and right-hand side (Claude Monet’s Houses of Parliament, Effect of Fog, 1899–1904) and described in Methods.

of an object in the foreground relative to that of the background

along nearly equal lines of sight. In order to objectively deﬁne

contrast in a manner that adapts to the scale and perspective

of an image, we use a wavelet technique. Wavelet analysis is

selected over Fourier analysis because it allows for quantifying

the local contrast in images (14), and was previously used to

estimate visibility in urban photographic images (15). We use a

Haar wavelet whereby ﬁrst differences of an image are taken at

various scales (16), ranging from individual pixels to spanning

the height or width of an image. An index of the contrast found

in an image is obtained by computing the 95th percentile of the

wavelet coefﬁcients, w

, normalizing by the median value, w

and taking the logarithm,

contrast index = log(w

). [1]

Normalization accounts for different baseline edge strengths

depending on lighting, scene, and image resolution, and the log-

arithm is suggested by the exponential dependence of contrast on

the extinction coefﬁcient (Methods). SI Appendix, Fig. S1 shows

four example paintings illustrating that the largest gradients relate

to distinct features, such as waves, a bridge, and the hull of ships.

Benchmarking with Photographs. We ﬁrst demonstrate our

metric for contrast on pairs of photographs taken during clear

and polluted conditions (SI Appendix, Fig. S2). These photo-

graphic pairs involve less artistic interpretation and allow for

benchmarking our technique using better-controlled image

characteristics. Consistent with our expectations, every polluted

photograph has a lower contrast index than its clear-sky counter-

part (SI Appendix, Fig. S2). The mean fractional reduction in the

contrast index from clear-sky to polluted photographs is 19%.

The same techniques used for photographs are next applied to

evaluate trends in contrast in paintings, which are then evaluated

in relation to aerosol emissions over time.

Quantifying Historical Air Pollution. As a proxy for historic

variations in anthropogenic aerosol concentrations, we use a

gridded estimate of annual emissions of sulfur dioxide, SO

(17). The early Industrial Revolution was largely powered by coal

(11, 18), and coal typically contains 1 to 5% sulfur by dry weight

(19). From 1800 to 1850, the United Kingdom emitted nearly

half of global SO

emissions, and the grid box corresponding

to London, known as the “Big Smoke” (9–11), accounts for

approximately 10% of all UK SO

emissions (Fig. 2), despite

accounting for only 1.0% of the area. SI Appendix, Fig. S3

presents qualitative evidence for the optical effects associated

with historical London air pollution captured by sketches and

photographs.

emissions are only a proxy for changes in atmospheric

environment on account of aerosol concentration and size

distribution at any particular time depending upon factors

including coemissions and local meteorology, e.g., refs. 20

and 21. Detrended British SO

emissions from 1800 to 1850,

spanning Turner’s artistic production, correlate with detrended

black carbon (r = 0.96) and organic carbon emissions (r = 0.95),

indicating that variability in SO

also generally tracks variability

in other aerosol emissions and, thus, total aerosol concentrations.

Later in the 19th century, however, the estimated emissions of

black carbon and organic carbon per unit coal in England begin

to decline (22). In London, in particular, political efforts to

reduce industrial pollution (11), shifts in cooking and heating

sources from coal to gas (18), and a more distributed urban

landscape (9) that was enabled by an expanded railway network

also likely contribute to decreasing peak aerosol concentrations

(10, 18). We thus expect the magnitude of aerosol concentration

associated with a given SO

emission rate to decrease over the

course of the 19th century.

Trends in Contrast in Paintings by Turner,

Monet, and Others

We examine the contrast of 60 oil paintings by Turner spanning

1796 to 1850 and 38 paintings by Monet spanning 1864

to 1901. Across Turner’s works (cataloged in SI Appendix,

Fig. S4), a progression is visually apparent from sharp to hazier

contours, more saturated to pastel-like coloration, and ﬁgurative

to impressionistic representation. A similar progression is evident

across Monet’s works (SI Appendix, Fig. S5), with the additional

factor that Monet’s paintings are from two distinct locations.

The ﬁrst 18 of Monet’s paintings, dating from 1864 to 1872,

depict scenes in or near Paris, and all but one were painted before

Monet’s ﬁrst visit to London from 1870 to 1871. The latter 20

paintings are from Monet’s 1899 to 1901 visits to London, where

he created serialized views of the House of Parliament, Waterloo

Bridge, and Charing Cross Bridge.

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Fig. 2. 19th century sulfur dioxide (SO

) emissions in London and Paris. (A) Time series of emissions (17) in the grid boxes encompassing London (blue) and

Paris (red). Years of paintings by Turner (T), Monet (M), Whistler (W), and Caillebotte, Pissarro, and Morisot (CPM) are indicated by horizontal lines for London

(blue) and Paris (red). Emissions during Monet’s early paintings correspond to those of Turner’s early paintings. (The dotted black line represents mean Parisian

emissions from 1864 to 1872). (B) A geographic distribution of SO

emissions in 1850, highlighting how emissions are concentrated in London (red point in

England) and that emissions in Paris (red point in France around 2

◦

E) trail those in London.

A mixed-effects model is used to evaluate whether local

emissions contribute to variations in contrast across our

collection of Turner and Monet paintings. In our baseline

formulation, we specify ﬁxed effects that capture variations in

contrast according to SO

emissions, year, and subject matter

categories. We also allow for an interaction between year and SO

to account for coemissions involved in producing atmospheric

haze proportionately declining over time (22). Finally, the 98

paintings in our collections are partitioned into three categories,

with 20 clear-sky, 46 cloudy, and 32 dawn or dusk paintings

(Methods).

Our baseline model explains 61% of the variance in the

contrast index (Fig. 3, SI Appendix, Table S1). As expected,

paintings depicting dawn or dusk conditions or cloudy conditions

have a lower contrast index (P < 0.01) relative to clear-sky

conditions. Moreover, the model shows a signiﬁcant reduction

in contrast in response to increases in SO

emissions (P < 0.01),

whereas the trend across years is indistinguishable from zero. The

interaction effect is also signiﬁcant (P < 0.01) and is consistent

with the emissions of SO

later in time yielding less change in

the contrast index.

Six other model speciﬁcations are also explored that indicate

that the signiﬁcance of the SO

contribution is robust to

excluding the year term or admitting for quadratic contributions

from SO

, year, or both (SI Appendix, Table S1). Our baseline

formulation is selected from among these models because it bal-

ances simplicity against the major features that we are concerned

with capturing. A means of selecting between models is offered by

the Bayesian information criteria (BIC), which measures model

performance as the difference between a reward term for better

predicting observations and a penalty term based on the number

of parameters that serves to guard against overﬁtting. A lower

BIC indicates a more apt model. Our baseline speciﬁcation

gives among the lowest BICs, though admitting for nonlinear

dependencies on year and SO

gives comparable values. The

least apt models, according to BIC, result from excluding SO

The primary reason that SO

, as opposed to year, is inferred

to control contrast relates to the fact that Paris and London

have distinct SO

emission histories (Fig. 2). The magnitude

of SO

emissions in London near the beginning of Turner’s

career in 1796 is similar to the magnitude of the emissions near

the beginning of Monet’s career in Paris in 1864. Monet’s early

paintings in Paris have higher contrast than most of Turner’s

works subsequent to the 1820s, despite coming later, such that

no simple time trend can be ﬁt across these collections (Fig. 3A). If

examined in the context of SO

emissions, however, the contrast

of Monet’s early works overlaps with those of Turner’s, and the

low contrast of Monet’s later works is in accord with the high

emissions in London at the end of the 19th century (Fig. 3B).

Monet and Turner are among the most proliﬁc and iconic

artists whose work spans the industrial era, but paintings by other

artists that depict cityscapes and atmospheric phenomena also

align with our proposed model. Speciﬁcally, our model predicts

the contrast found in seven paintings by Gustave Caillebotte

(1848 to 1894), four paintings by Camille Pissarro (1830 to

1903), and one painting by Berthe Morisot (1841 to 1895)

of Paris on the basis of year, local SO

emissions, and subject

matter (Fig. 3). The contrast indices calculated for six Nocturnes

paintings by Whistler in London between 1871 and 1875 are

also predicted by our model. Note that Whistler’s paintings in

less-polluted environments—for example, The Coast of Brittany

(1861) or The Blue Wave Biarritz (1862)—are associated with

substantially greater contrast indices of 2.4 and 2.2, respectively.

Reﬁtting the mixed-effects model to our expanded dataset

including works by Caillebotte, Pissarro, Morisot, and Whistler

leads to conclusions that are consistent with our more limited

analysis of only works by Turner and Monet (Fig. 3). The year

trend inferred from our expanded analysis, however, appears

signiﬁcant only in the case where SO

is entirely excluded

(SI Appendix, Table S1), further supporting the importance

of SO

Trends in Intensity

As a complementary approach, it is also possible to analyze the

intensity of images across our collection of works. Aerosols scatter

visible light of all wavelengths into the line of sight (23) (Fig. 1),

leading to a whiter tint and increased light intensity during

daytime (13). We examine the relationship between intensity

and SO

emissions using the same mixed-effects methodology

used for contrast and ﬁnd a signiﬁcant effect (P < 0.01) of

emissions increasing intensity in our baseline approach

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Fig. 3. Trends in the contrast index for diﬀerent subject matter in the 60 Turner paintings (red) and 38 Monet paintings (blue) versus (A) year or (B) SO

emissions local to London or Paris. Also shown are six Whistler Nocturnes paintings (cyan), seven paintings by Caillebotte, four by Pissarro, and one by Morisot

(gold). Paintings are categorized according to depicting conditions that are predominantly clear-sky (circle), cloudy (square), and dawn or dusk (triangle). Model

predictions (black horizontal lines) are shown along with their 5 to 95% uncertainty (black vertical bars). Trends (gray lines) are illustrated by allowing year

and SO

to vary but withholding categorical fixed eﬀect. Monet’s London paintings are plotted using 1899 London emissions because paintings were begun in

the winter of 1899 to 1900, although exhibited in the following years, up until 1904. SO

is plotted on a logarithmic scale. Also shown are four representative

paintings: (C) Turner’s Apullia in Search of Appullus (1814), (D) Turner’s Rain, Steam, and Speed (1844), (E) Monet’s Houses of Parliament, Sunlight Effect (1899, in the

Brooklyn Museum), and (F ) Monet’s Charing Cross Bridge (1899, in Madrid’s Thyssen-Bornemisza Museum), with their values also highlighted in (A) and (B), as

solid markers labeled with their panel letter.

(SI Appendix, Table S2 and Fig. S6). Of the 12 other speciﬁcations

including SO

, 9 show signiﬁcant effects (P < 0.05), including

all those conditioned on the larger set of artists. For the paired

photographic analysis (SI Appendix, Fig. S2), polluted photos

have a uniformly increased intensity index, consistent with the

analysis of contrast, but in this case, averaging 39% greater. The

interpretation of intensity trends is complicated, however, in

that variations in image intensity may result from accumulation

of residue, fading of pigments, or photographic techniques (24),

in addition to optical effects created by aerosols, such that we

consider intensity secondary to contrast for purposes of indicating

optical effects.

Visibility can be inferred from intensity using an empirical

relationship (Methods). Our estimates indicate that before 1830,

visibility in clear-sky and cloudy Turner paintings averaged

25 km, whereas it decreased to an average of 10 km after 1830.

For early Monet paintings, visibility averages 24 km, and for

Monet’s daytime paintings in London, visibility averages 6 km

(SI Appendix, Fig. S7). In comparison, (25) estimated visibility

using the furthest clearly visible feature in 35 of Monet’s Charing

Cross Bridge paintings and found a mean of 1 km. They note

that the London Fog Inquiry describes visibility in the winter

of 1901 to 1902 as never being more than approximately

2 km. Differences could arise due to uncertainties in both

methodologies— the imprecision of estimating visibility by eye

for (25) and, in addition to the aforementioned issues with

interpreting intensity, there are various limiting assumptions in

our model of visibility (Methods).

Style vs. Environment

It is clear that industrialization changed the environmental

context in which painting occurred. Indeed, 19th century art

critic John Ruskin wrote about Turner’s work that, “had the

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weather when I was young been such as it is now, no book

such as ‘Modern Painters’ ever would or could have been

written” (26). A primary question, however, is the degree to

which trends toward decreased contrast and increased intensity

represent physical, optical changes associated with a polluted

atmosphere, as opposed to exerting an indirect inﬂuence on

artistic style. Beyond the statistical results discussed earlier, two

further considerations suggest that environmental trends are

rendered in the works we consider.

First, the environment that these artists depict was, in fact,

subject to large trends in atmospheric pollution (17). Turner was

born in the age of sail and died in an age of coal and steam (27).

It is important to recognize, however, that not all artists depict a

changed atmospheric environment. For example, John Constable

(1776 to 1837) created works that show neither the diminished

contrast nor increased intensity expected from London’s aerosol-

laden atmosphere. It may be that certain artists chose times and

locations where the effects of pollution were minimal. Indeed,

while Constable remarked that Turner seems to paint with “tinted

steam” (28), he himself was known to leave London for less-

polluted Hampstead Heath or the Lake District (29).

The second consideration is more speculative as it relates to

the intention of Turner and Monet to depict environmental

change. We focus on Turner in this section and Monet in

the next. Turner spoke about ﬁnding artistic material in his

environment: “nature dispensing incidents for the artist’s study...

to store in his mind with every change of time and place” (30).

More speciﬁcally, Turner sought to represent technological and

resulting environmental change (27), especially as it relates to

atmospheric effects on light. In The Fighting Temeraire (1839),

perhaps Turner’s most iconic work, a steam-powered tugboat

pulls the HMS Temeraire, a military sailing ship made famous

by the 1805 Battle of Trafalgar, to land to be broken up for

scrap against a backdrop of a ﬁery setting sun, illustrating the

transition from the age of sail to steam. Similarly, Rain, Steam,

and Speed (1844) depicts a train racing through the British

countryside, contrasted with symbols of the past age, such as

a row boat gliding over the water, a hare, the fastest natural

animal in Britain, running from the oncoming train, and a

farmer plowing without mechanized equipment, all almost lost

in mist.

That Turner should be among the ﬁrst to depict changes

in how light transmits through a polluted atmosphere might

be traced to a general increase in the interest in and scientiﬁc

understanding of light and the sky that occurred during his

lifetime (31). In 1801, astronomer William Herschel gave a

lecture, “The Nature of the sun” (32), which is thought to have

inﬂuenced how Turner paints the brightness and texture of the

Sun (27) (SI Appendix, Fig. S8A). In 1803, meteorologist Luke

Howard published On the Modiﬁcation of Clouds that introduced

the cloud classiﬁcation of cumulus, stratus, and cirrus (33),

which was featured in art manuals and even inspired a poem by

Johann Wolfgang von Goethe (1749 to 1832) (29). SI Appendix,

Fig. S8B shows cloud studies by Luke Howard, and, roughly

synchronously, by Turner.

Turner’s documentation of the optical effects of aerosols is also

on display in the context of explosive volcanic eruptions. Turner’s

paintings show changes in sunset coloration that accord with the

expected effects of volcanic eruptions injecting aerosols into the

stratosphere (5, 6). Turner also produced a sketchbook of 65

watercolors of sunsets in the three years following the Tambora

eruption that captures the waxing and waning of the atmo-

spheric reddening associated with stratospheric volcanic aerosols

(SI Appendix, Fig. S8C). The fact that the course of events

that Turner documents is consistent with the expected timescale

associated with stratospheric aerosol migration and deposition

following a volcanic eruption, (i.e., 1 to 3 y, 34) is further

evidence for Turner providing a faithful depiction of variations

in atmospheric light phenomena.

Additional Considerations for Inferring

Pollution from Paintings

If it is accepted that the optical consequences of increased

atmospheric pollution are depicted in certain works, a question

arises whether it is possible to calibrate the depicted trends for

making inferences regarding atmospheric composition. Although

such a reconstruction would be useful because there are no direct

quantitative measurements of urban air pollution during early

industrialization (35), any such inference is challenging. The

mechanisms associated with recording environmental conditions

using paint and canvas are, arguably, of similar complexity to how

any natural proxy records the environment, such as tree rings or

ice cores. The aesthetic considerations associated with works of

art then add additional layers of interpretation. One issue is that

Monet and Whistler appear to have been inﬂuenced by Turner’s

style (36). Turner’s Rain, Steam, and Speed, for example, was one

of the few paintings by other artists that Monet directly referred

to in his correspondence (37).

A related issue in considering whether atmospheric compo-

sition can be inferred from certain paintings is that the scenes

sampled in these works are not chosen at random. Monet, for

example, wrote about the role of air pollution in his creative

process, “What I like most of all in London is the fog” (11)

and, “when I got up I was terriﬁed to see that there was no fog,

not even a wisp of mist: I was prostrate, and could just see all

my paintings done for, but gradually the ﬁres were lit and the

smoke and haze came back” (38). Note that the word “smog”

for smoke and fog was not coined until 1905 (11). Insomuch

as Monet focused on the atmospheric effects associated with

high aerosol concentrations, trends in painting characteristics

may reﬂect changes in extreme events, as opposed to reﬂecting

changes in average conditions.

There is some evidence that Monet chose to paint on days

when ambient air pollution would have been higher on account

of meteorological conditions. Given some amount of aerosol

precursor emissions, ambient air pollution concentrations tend

to be higher when surface winds are weak, surface pressure is high,

and precipitation is absent (39, 40). This meteorological pattern

was already speculated upon in the context of “London Fogs”

around the time that Monet was painting (41). Monet’s letters

indicate that he painted on 26 February and 4, 7, and 9

March 1900 (38), and the corresponding daily weather reports

for London (42) indicate that these days are associated with

weaker wind speeds (varying from 1 to 3 out of 12 in the

Beaufort wind scale), relatively higher atmospheric pressure (with

phrases like “barometer rising” or “bar rising slowly”), and

essentially no precipitation (noted as 0 inches on these days,

except 0.01 inches on 26 February). It is also well established

that air pollution concentrations are higher in winter than

summer because of a shallower planetary boundary layer and

because atmospheric stability can be higher on account of

capping inversions (43), consistent with Monet visiting and

painting London in winter and spring months. Information

is lacking, however, regarding the speciﬁc time, date, or dates

individual paintings depict, such that we do not explicitly

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account for meteorological reports or seasonal dependencies in

our analysis.

We also mention a hypothesis that ascribes trends in works to

increasingly faulty vision (44). It appears that loss of visual acuity

associated with the development of cataracts led Edgar Degas

(1834 to 1917) to paint with a different palette and in less detail

(45). There is no direct evidence, however, that Turner, or Monet

for that matter, had eyesight conditions that would translate into

hazier paintings styles (46). Turner continued to paint details

in the foreground throughout his life, and (46) demonstrates

that Monet was not myopic and that he suffered from cataracts

decades after he began painting more impressionistic works.

Conclusions

Our basic premise is that Impressionism—as developed in the

works of Turner, Monet, and others—contains elements of

polluted realism. Over the 19th century, the atmospheric reality

in London and Paris changed. Turner, Monet, and others

document these changes in paint, yielding proxy evidence for

historical trends in atmospheric pollution before instrumental

measurements of air pollution become available. A mixed-effects

model including both temporal and environmental trends can

explain 61% of the variance in a contrast index and gives a

signiﬁcant dependence on SO

emissions for each statistical

model speciﬁcation, including after controlling for year and

subject matter. These results indicate that a combination of

trends in style and atmospheric pollution contribute to trends

in the contrast of Turner and Monet’s paintings. The magnitude

of the changes in paintings is plausible relative to changes between

contemporary pairs of clear-sky and polluted photographs.

Estimates of intensity generally correspond to those of contrast

but are noisier and less signiﬁcant. Visibility inferred from the

London works by Monet is also in keeping with historical records

of visibility.

Issues associated with scene selection and the atmospheric

chemistry of smog would need to be controlled for be-

fore quantitative inferences of mean atmospheric condi-

tions are possible from this sample of paintings. Never-

theless, the evidence that we present for Turner, Monet,

and others depicting physical atmospheric conditions pro-

vides additional, complementary opportunities for appreci-

ating and interpreting their artwork. Our view is that

impressionistic paintings recording natural phenomena—as op-

posed to being imagined, amalgamated, or abstracted—does not

diminish their signiﬁcance; rather, it highlights the connection

between environment and art. Furthermore, our results suggest

that environmental change provided a creative impulse whereby

the importance of lines and edges was diminished in favor of

demarcating objects using color ﬁelds.

A historical connection between aerosols and painting style

may also afford some perspective on cultural responses to

contemporary human-caused environmental changes. Megacities

such as Beijing, New Delhi, and Mexico City have levels of

air pollution similar to those of 19th century London (47).

Furthermore, if stratospheric solar radiation management were

used to mitigate climate risk (48, 49), it would increase the

intensity, or whiteness, of the sky and globally diminish the

contrast of objects viewed against this background. Our ﬁndings

suggest that modern changes to atmospheric properties can also

be expected to both literally and ﬁguratively change how we see

the world.

Materials and Methods

Theoretical Expectations of Contrast and

Visibility

As the distance, dx, between an observer and object increases, the intensity of

light from the object, I

(x), increases as a result of diffuse background light

scattered into the line of sight, σ

(x), and decreases as a result of scattering

and absorption along the line of sight, σ

(x),

(x)

= σ

(x) − σ

(x). [

]

When particles are present, dx is proportional to the number of suspended

aerosol particles in the air column. The isotropic scattering coefﬁcient, σ

represents the efﬁciency with which background light is scattered into the line

of sight, and the extinction coefﬁcient, σ

, represents how much intensity is lost

through absorption and scattering as a beam of light passes through a material.

Both coefﬁcients are in units of inverse meters.

Unlikefor a ﬁniteobject, background radiationis assumedto be independent

of x, given homogeneous, isotropic background scattering, and therefore,

(x)

= σ

(x) − σ

(x) = 0. [

]

It follows that σ

equals σ

Replacing σ

with σ

in Eq. 2, integrating intensity from 0 to I and distance

from 0 to X, and taking the logarithm yields

(x) − I

(x)

= exp(−σ

X). [

]

The left-hand side of Eq. 4 is deﬁned as the contrast, C(x), or the relative

difference between I

(x) and I

(x),

C(x) = exp(−σ

X). [

]

A black object at a distance x = 0, for instance, has I

(0) = 0, by deﬁnition,

yielding a contrast of one. Eq. 4 is a version of the Beer–Lambert law where

contrastdecreases exponentiallywith distancefrom anobject, dx, orwith particle

concentration when particles are present. Assumed in this representation is that

both the backgroundand object intensities are seenalong nearly the same lines

of sight and that the background intensity is independent of direction (12).

Taking the logarithm of Eq. 5 and rearranging yields,

X =

− ln(C(x))

. [

]

Earlier studies assumed that a contrast threshold of C(x) = 0.02 was the

perceptible limit or farthest distance one can detect a dark object against a light

background (50). We follow more-recent studies in using a contrast threshold of

0.05 (51, 52), yielding a highly idealized estimate of visibility, X

, as an inverse

function of the extinction coefﬁcient, σ

3.0

, [

]

known as the Koschmieder equation (50).

Intensity and Visibility. We quantify the amount of white light using the

hue–saturation–intensity color model, where intensity ranges from black, with a

value of zero, to whitewith avalue of one(53). Weconsider themedian intensity

across all image pixels, referred to as the intensity index. The image median is

simple to deﬁne, though an analysis of only the sky or other common features

could also be instructive.

It is possible to estimate σ

from anomalies in intensity using an empirical

function derived from photographic observations (13),

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= 3.4 × 10

−7

exp



14.7(

I −

)



+ 1.1 × 10

−4

. [

]

I is the image-median intensity, which ranges from 0.1 to 0.8 across the images

of paintings that we consider (SI Appendix, Fig. S6) and between 0.3 to 0.9

among the urban photographs we consider (SI Appendix, Fig. S2). Depending

on the application of Eq. 8,

is the 95th percentile of the clear-sky paintings

in our collection, 0.65, or the 95th percentile of clear-sky photographs in our

collection, 0.75. Note that Eq. 8 is rewritten from (13) to be in units of inverse

meters and to depend on intensity scaled between 0 and 1.

A less-idealized estimate of σ

would be possible taking into account solar

geometry, the position of the observer, and the direction of view, but such

information is not readily available for most of the paintings we consider. The

images we consider, moreover, are not digitized under identical conditions,

which inevitably introduces noise to the samples.

Substituting Eq. 8 for σ

into Eq. 7 gives an estimate of the visibility range

associated with various paintings (SI Appendix, Fig. S7). For early Turner works,

our visibility ranges are broadly consistent with ranges of 20 to 30 km for

contemporary clear-sky urban conditions (54), and for Turner’s later works, as

well as Monet’s London paintings, visibility ranges are consistent with the 1

to 5 km range estimated for contemporary strong urban haze conditions (13).

Visibility estimates below 5 km for late 19th century London are in keeping with

estimates for contemporary megacities during strong urban haze conditions,

such as Delhi (55), and Beijing (56, 57).

Fractional changes in contrast and intensity indices are also calculated for

comparison with photographs. This percent change is calculated only for Turner

paintings (e.g., by subject matter, such as predominantly clear-sky) by dividing

thesepaintings intotwo groupsandcomputing, (I

late

−I

early

)/I

early

×100,

wherein paintings in each category are divided into two equally sized groups

for early and late. For photographs, the percent change is calculated between

clear-sky and polluted photographs.

Wavelet Analysis of Contrast. Wavelet analysis is performed by convolving

grayscaleimage matrices with atwo-dimensional, multiscale Haarwavelet using

the Python package, PyWavelets and, speciﬁcally, the wavedec2 function (58).

The Haar wavelet consists of a hierarchy of square-wave–shaped functions,

ψ(t) =











1 0 ≤ t <

−1

≤ t < 1,

0 otherwise,

[

]

Grayscale image matrices arecalculated asa weighted sum of the corresponding

red, green, and blue pixels, X = 0.2125R + 0.7154G + 0.0721B, though

results are qualitatively similar for individual color channels.

High-pass or detailed coefﬁcients are interpreted because of interest in the

representation of abrupt features. Seven scales are used for the Haar wavelets,

although similar results are obtained using fewer scales. Coefﬁcients can be

computed in the horizontal, vertical, and diagonal directions, and we use

horizontal coefﬁcients that emphasize horizontal edges (59).

An index of the contrast found in an image is computed as the 95th-

percentile of all high-pass horizontal coefﬁcients divided by the median (Eq. 1

in Optical Implications of Increasing Aerosol Concentrations). Selection of the

95th-percentile represents a balance between identifying among the sharpest

features in an image and guarding against being overly sensitive to outliers.

Using other high percentiles, however, such as the 90th or 99th, gives similar

results. Normalization by the median of the coefﬁcients accounts for different

baseline edge strengths depending on lighting, scene, and image resolution.

Mixed-Effects Model Formulation

Our baseline mixed-effects model, for the contrast index is formulated as

follows:

log(contrast) = α

+ α

year + α

+ α

(type) + α

year∗SO

+ 

[

]

See ref. (60) for an overview of mixed-effects models. This model involves ﬁxed

effect terms for year, SO

, the interaction between yearand SO

, and categorical

effects associated with subject matter (type) according to clear-sky, cloudy, or

dawn/duskconditions. Allowingfor randomintercepts orslopes, suchas fortype,

does not improve the ﬁt as judged by either the Akaike or Bayesian information

criteria,consistentwithour priorexpectationofanonzero contributiontocontrast

from each factor. The year is represented as the anomaly from the mean. The

interaction term between year and SO

captures the weakening effect of a given

amount of SO

to the total aerosol concentration (Quantifying Historical Air

Pollution). This interaction term is positive and offsets the negative contribution

to contrast solely from SO

, which overpredicts decreases in contrast when SO

is considered individually, without the interaction term.

This model formulation allows for examining how SO

emissions inﬂuence

contrast across paintings after controlling for effects associated with temporal

trends and selection of subject matter. Although year and SO

are correlated

= 0.47), the fact that London and Paris have different SO

time

histories permits for distinguishing simple time trends from environmental

trends.

Results for our primary speciﬁcations are given in SI Appendix, Table S1

including only works by Turner (60) and Monet (38) on row 1 and for also

including 6 works by Whistler, 7 by Caillebotte, 4 by Pissarro, and 1 by

Morisot on row 7. Six alternative speciﬁcations are also examined: omitting

the interaction term between year and SO

, omitting the interaction and SO

terms, omitting the interaction and year terms, omitting the interaction term

but including a term for SO

, omitting the interaction term but including

year

, and omitting the interaction term but including both year

and SO

Speciﬁcations8to14areequivalentto1to 7butappliedtothelargercollectionof

116 paintings.

Similarly, a linear model for the intensity index is formulated as follows:

intensity = β

+ β

year + β

+ β

(type) + β

year ∗ SO

+ 

. [

]

SI Appendix, Table S2 reports intensity results for the baseline formulations for

Turner and Monet paintings (row 1) and for all paintings (row 7) as well as six

alternative speciﬁcations as for contrast (14 speciﬁcations total; seven for Turner

and Monet paintings and another seven the larger collection of paintings).

Data, Materials, and Software Availability. Painting, airpollution data have

been deposited in Harvard Dataverse (https://doi.org/10.7910/DVN/YQOLZW).

All study data are shown in the article and/or SI Appendix.

ACKNOWLEDGMENTS. We would like to thank art historians Scott Allan,

Fabienne Chevallier, and James Rubin and atmospheric scientists Kerry

Emanuel, Roger Fouquet, Bernhard Mayer, and RobinWordsworth for insightful

feedback that improved earlier versions of this manuscript. We also thank Jen

Thum and the Harvard Art Museums for helping facilitate a course on art and

environment.

Author aﬀiliations:

Laboratory of Dynamic Meteorology, Sorbonne University, École

normale supérieure, Paris 75005, France; and

Department of Earth and Planetary

Sciences, Harvard University, Cambridge, MA 02138

1. S. M. Fikke, J. E. Kristjánsson, Ø. Nordli, Screaming clouds. Weather 72, 115–121 (2017).

2. D. W. Olson, R. L. Doescher, M. S. Olson, Dating van Gogh’s “Moonrise”. Sky & Telescope 106,

54–58 (2003).

3. J. Baker, J. E. Thornes, Solar position within Monet’s Houses of Parliament. Proc. R. Soc. A: Math.

Phys. Eng. Sci. 462, 3775–3788 (2006).

4. H. Neuberger, Climate in art. Weather 25, 46–56 (1970).

5. C. S. Zerefos, V. Gerogiannis, D. Balis, S. Zerefos, A. Kazantzidis, Atmospheric effects of volcanic

eruptions as seen by famous artists and depicted in their paintings. Atmos. Chem. Phys. 7,

4027–4042 (2007).

6. C. Zerefos et al., Further evidence of important environmental information content in red-to-green

ratios as depicted in paintings by great masters. Atmos. Chem. Phys. 14, 2987–3015 (2014).

7. A. Meinel, M. Meinel, Sunsets, Twilights, and Evening Skies (1991).

PNAS 2023 Vol. 120 No. 6 e2219118120 https://doi.org/10.1073/pnas.2219118120 7 of 8

Downloaded from https://www.pnas.org by 24.161.50.3 on March 11, 2023 from IP address 24.161.50.3.

8. C. von Savigny, A. Lange, A. Hemkendreis, C. G. Hoffmann, A. Rozanov, Is it possible to estimate

aerosol optical depth from historic colour paintings? Clim. Past. 18, 2345–2356 (2022).

9. P. Brimblecombe, London air pollution, 1500–1900. Atmos. Environ. 1967, 1157–1162 (1977).

10. P. Brimblecombe, The Big Smoke: A History of Air Pollution in London Since Medieval Times

(Routledge, 2012).

11. C. L. Corton, London Fog (2015).

12. H. Horvath, On the applicability of the Koschmieder visibility formula. Atmos. Environ. 1967,

177–184 (1971).

13. K. W. Kim, Y. J. Kim, Perceived visibility measurement using the HSI color difference method. J.

Korean Phys. Soc. 46, 1243 (2005).

14. G. Strang, T. Nguyen, Wavelets and Filter Banks (SIAM, 1996).

15. C. H. Luo et al., Investigation of urban atmospheric visibility Hsing Haar wavelet transform. Aerosol

Air Qual. Res. 5, 39–47 (2005).

16. A. Haar, Zur theorie der orthogonalen funktionensysteme (1909).

17. R. M. Hoesly et al., Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols

from the community emissions data system (CEDS). Geosci. Model Dev. 11, 369–408 (2018).

18. R. Fouquet, Long run trends in energy-related external costs. Ecol. Econ. 70, 2380–2389 (2011).

19. W. H. Calkins, The chemical forms of sulfur in coal: A review. Fuel 73, 475–484 (1994).

20. H. He et al., Mineral dust and NOx promote the conversion of SO

to sulfate in heavy pollution

days. Sci. Rep. 4, 1–6 (2014).

21. B. Zheng et al., Heterogeneous chemistry: A mechanism missing in current models to explain

secondary inorganic aerosol formation during the January 2013 haze episode in North China.

Atmos. Chem. Phys. 15, 2031–2049 (2015).

22. C. Junker, C. Liousse, A global emission inventory of carbonaceous aerosol from historic records of

fossil fuel and biofuel consumption for the period 1860–1997. Atmos. Chem. Phys. 8, 1195–1207

(2008).

23. O. Boucher, Atmospheric Aerosols (2015).

24. K. Martinez, J. Cupitt, D. Saunders, R. Pillay, Ten years of art imaging research. Proc. IEEE 90,

28–41 (2002).

25. J. E. Thornes, G. Metherell, “Monet’s ‘London series’ and the cultural climate of London at the turn

of the Twentieth century” in Weather, Climate, Culture (2003), pp. 141–160.

26. J. Ruskin, The Art of England: Lectures Given in Oxford (George Allen, 1884).

27. J. Hamilton, Turner and the Scientists (1998).

28. Tate, Turner’s modern world (2021).

29. R. Hamblyn, The Invention of Clouds: How an Amateur Meteorologist Forged the Language of the

Skies (Pan Macmillan, 2002).

30. J. E. Thornes, A brief history of weather in European landscape art. Weather 55, 363–375 (2000).

31. S. T. Reno, Air: Clouds and Climate Change in the Nineteenth Century (2020).

32. W. Herschel, XIII. Observations tending to investigate the nature of the sun, in order to ﬁnd the

causes or symptoms of its variable emission of light and heat; with remarks on the use that may

possibly be drawn from solar observations. Philos. Trans. R. Soc. Lond. 91, 265–318 (1801).

33. L. Howard, On the Modiﬁcation of Clouds’ (Essay to the Askesian Society. Taylor, London, 1803).

34. A. Robock, Volcanic eruptions and climate. Rev. Geophys. 38, 191–219 (2000).

35. D. Fowler et al., A chronology of global air quality. Philos. Trans. R. Soc. A 378, 20190314

(2020).

36. T. Britain, Turner, Whistler, Monet, press release (2005). https://www.tate.org.uk/press/press-

releases/turner-whistler-monet.

37. E. Shanes, Impressionist London (1994).

38. S. Patin, Claude Monet in Great Britain (Hazan, 1994).

39. H. Zhang, Y. Wang, J. Hu, Q. Ying, X. M. Hu, Relationships between meteorological parameters

and criteria air pollutants in three megacities in China. Environ. Res. 140, 242–254 (2015).

40. Y. Liu, Y. Zhou, J. Lu, Exploring the relationship between air pollution and meteorological

conditions in China under environmental governance. Sci. Rep. 10, 1–11 (2020).

41. W. Shaw, The London fog inquiry. Nature 64, 649–650 (1901).

42. P. M. Craig, E. Hawkins, Digitizing observations from the Met Ofﬁce daily weather reports for

1900–1910 using citizen scientist volunteers. Geosci. Data J. 7, 116–134 (2020).

43. R. B. Stull, An Introduction to Boundary Layer Meteorology (Springer Science and Business Media,

1988), vol. 13.

44. R. Liebreich, Turner and Mulready: The Effect of Certain Faults of Vision on Painting with Especial

Reference to Their Works; the Real and Ideal in Portraiture; the Deterioration of Oil Paintings (1888).

45. M. F. Marmor, Ophthalmology and art: Simulation of Monet’s cataracts and Degas’ retinal disease.

Arch. Ophthalmol. 124, 1764–1769 (2006).

46. M. Marmor, Vision, eye disease, and art: 2015 Keeler Lecture. Eye 30, 287–303 (2016).

47. D. Mage et al., Urban air pollution in megacities of the world. Atmos. Environ. 30, 681–686

(1996).

48. D. W. Keith, Geoengineering the climate: History and prospect. Ann. Rev. Energy Environ. 25,

245–284 (2000).

49. P. J. Crutzen, Albedo enhancement by stratospheric sulfur injections: A contribution to resolve a

policy dilemma? Clim. Change 77, 211 (2006).

50. H. Koschmieder, Theorie der horizontalen Sichtweite. Beitrage zur Physik der freien Atmosphare

33–53 (1924).

51. J. I. Gordon, Daytime visibility, a conceptual review (1979).

52. M. Jarraud, Guide to meteorological instruments and methods of observation (WMO-No. 8). World

Meteorol. Organ.: Geneva, Switzerland 29 (2008).

53. A. Vidovszky-Németh, J. Schanda, White light brightness-luminance relationship. Light. Res.

Technol. 44, 55–68 (2012).

54. A. Singh, W. J. Bloss, F. D. Pope, 60 years of UK visibility measurements: Impact of meteorology

and atmospheric pollutants on visibility. Atmos. Chem. Phys. 17, 2085–2101 (2017).

55. A. Singh, S. Dey, Inﬂuence of aerosol composition on visibility in megacity Delhi. Atmos. Environ.

62, 367–373 (2012).

56. P. Zhao, X. Zhang, X. Xu, X. Zhao, Long-term visibility trends and characteristics in the region of

Beijing, Tianjin, and Hebei, China. Atmos. Res. 101, 711–718 (2011).

57. Q. Zhang et al., Effects of meteorology and secondary particle formation on visibility during heavy

haze events in Beijing. China. Sci. Total Environ. 502, 578–584 (2015).

58. G. Lee, R. Gommers, F. Waselewski, K. Wohlfahrt, A. O’Leary, Pywavelets: A python package for

wavelet analysis. J. Open Source Softw. 4, 1237 (2019).

59. D. Mistry, A. Banerjee, Discrete wavelet transform using MATLAB. Int. J. Comput. Eng. Technol.

(IJCET) 4, 252–259 (2013).

60. W. W. Stroup, Generalized Linear Mixed Models: Modern Concepts, Methods and Applications (CRC

Press, 2012).

8 of 8 https://doi.org/10.1073/pnas.2219118120 pnas.org

Downloaded from https://www.pnas.org by 24.161.50.3 on March 11, 2023 from IP address 24.161.50.3.

Discussion

> "As a complementary approach, it is also possible to analyze the intensity of images across our collection of works. Aerosols scatter visible light of all wavelengths into the line of sight (23) (Fig. 1), leading to a whiter tint and increased light intensity during daytime." Aerosols are tiny particles or droplets suspended in the atmosphere. They can scatter and absorb light, which can influence the way objects appear in the environment. When aerosols are present in the air, they can reduce the contrast and visibility of objects by scattering and absorbing some of the light that would otherwise be reflected by the objects. This scattering can cause the light to be diffused, resulting in a hazy or smoggy appearance. Aerosols can also alter the intensity of light, making it appear brighter or dimmer than it would without the presence of aerosols. Therefore, the amount of aerosols in the atmosphere can have a significant impact on how objects are perceived visually. > “It is clear that industrialization changed the environmental context in which painting occurred. Indeed, 19th century art critic John Ruskin wrote about Turner’s work that, “had the weather when I️ was young been such as it is now, no book such as ‘Modern Painters’ ever would or could have been written”.” > "Our basic premise is that Impressionism—as developed in the works of Turner, Monet, and others—contains elements of polluted realism. Over the 19th century, the atmospheric reality in London and Paris changed. Turner, Monet, and others document these changes in paint, yielding proxy evidence for historical trends in atmospheric pollution before instrumental measurements of air pollution become available." > This paper discusses how changes in the environment due to industrialization during the 19th century affected the styles and techniques of artists such as J.M.W. Turner and Claude Monet. The increased emissions of anthropogenic aerosols resulted in an optical environment with less contrast and more intensity, which is reflected in the paintings. The study shows that changes in local sulfur dioxide emissions are a highly significant explanatory variable for trends in the contrast and intensity of Turner, Monet, and others’ works, indicating that their paintings captured changes in the optical environment associated with increasingly polluted atmospheres during the Industrial Revolution. It is important to benchmark the wavelet method with photographs which have less artistic interpretation and can control for image characteristics. This is extreme. 20th/21st century emissions have also been heavily unequal among different countries, but this is next level. > "From 1800 to 1850, the United Kingdom emitted nearly half of global SO2 emissions" > "Some works of art, even those that do not appear “realistic,” appear to faithfully record particular natural phenomena." During his stays in London between the autumn of 1899 and the early months of 1900 and 1901, Claude Monet painted a series of about 100 impressionist oil paintings depicting various views of the Thames River. One of these series focused on the Palace of Westminster, which is the home of the British Parliament. Monet began the first painting of this series on February 13th, 1900, at around 3:45 pm. All the paintings in this series were painted from the same vantage point, either from Monet's window or a terrace at St Thomas' Hospital overlooking the Thames. The paintings are painted under different weather conditions and times of the day. Source: https://en.wikipedia.org/wiki/Houses_of_Parliament_(Monet_series) > "Furthermore, Turner and Monet’s works span the Industrial Revolutions starting in Great Britain in the late 18th century, a time of unprecedented growth in air pollution (9–11). Over the course of their careers, Turner and Monet’s painting styles change from sharper to hazier contours and toward a whiter palette, a progression that is typically characterized as moving from a more ﬁgurative to impressionistic style. We explore the hypothesis that increasingly impressionistic paintings by Turner, Monet, and several other artists represent, at least in part, physical changes in atmospheric optical conditions."

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