Diurnal Patterns in Global Fine Particulate Matter Concentration

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Diurnal Patterns in Global Fine Particulate Matter Concentration Max Manning, Randall V. Martin, Christa Hasenkopf, Joe Flasher, and Chi Li Environ. Sci. Technol. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.estlett.8b00573 • Publication Date (Web): 02 Nov 2018 Downloaded from http://pubs.acs.org on November 4, 2018

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Environmental Science & Technology Letters

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Diurnal Patterns in Global Fine Particulate Matter Concentration

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Max I. Manning1,*, Randall V. Martin1,2, Christa Hasenkopf 3, Joe Flasher3, Chi Li1

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[1] Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS B3H

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4R2, Canada

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[2] Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics,

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Cambridge, MA 02138, USA

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[3] OpenAQ, Washington, D.C. 20001, USA

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*Corresponding

Author: Max I. Manning. Tel: +16043289528, e-mail: [email protected]

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For table of contents only

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Abstract Limited information is available on how fine particulate matter (PM2.5) concentrations

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vary over the course of a day at the global scale. We used aggregated data sources to analyze the

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diurnal cycle of PM2.5 from over 17 million hourly measurements at 3110 sites across the world,

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primarily in North America, Europe, and East Asia. The measurements reveal that diurnal PM2.5

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cycles exhibit similar patterns across sites worldwide with a mean diurnal variability of 13.1%.

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PM2.5 cycles have a morning peak between 7:00-10:00 local solar time (LST) and an early night

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time peak between 21:00-23:00 LST, with an afternoon minimum occurring between 15:001 ACS Paragon Plus Environment


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17:00 LST. Diurnal cycles are associated with dynamics of the atmospheric mixed layer as well

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as diurnal emission patterns, exhibiting increased amplitude in regions with large mixed layer

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height and mountainous terrain.

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1. Introduction

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Airborne fine particulate matter (PM2.5) is of global interest due to its adverse health

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effects1 and its effects on climate2. PM2.5 exposure has been identified as the leading

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environmental risk factor for the global burden of disease with 4.2 million attributable deaths in

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20153. PM2.5 concentrations are influenced by the complex effects of anthropogenic and natural

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sources, meteorology, and chemical processes4,5. Understanding the diurnal variation of PM2.5 is

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important to understand patterns of exposure, to evaluate and improve its representation in

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models, to relate satellite observations at specific overpass times to 24-hour concentrations, and

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to design PM2.5 measurement systems. Until recently, much of the available data on PM2.5 has

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had coarse temporal resolution or covered a limited geographic area; to our knowledge no studies

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have analyzed the measured diurnal PM2.5 cycle at the global scale.

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Several single-city analyses have been conducted of PM2.5 diurnal variation6–9. Diurnal

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PM2.5 cycles modulate other modes of PM2.5 variability10,11 and are thought to be driven by local

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emissions, meteorology and secondary production12. PM2.5 can also affect atmospheric

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stability13. Cycles from different sites within regions share notable features, such as morning and

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evening peaks which have been associated with periods of increased anthropogenic activity14,15.

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Questions remain about the consistency of these cycles both within and between regions.

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PM2.5 measurements in developing countries have historically been sparse and

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disaggregated. This has begun to change in recent years due to government agencies and

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research groups across the world collecting and publicly sharing air quality data in near real time.

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However the data are provided in many different formats and often historical data are not

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retained. OpenAQ is a non-profit organization that aggregates, uniformly formats, and retains

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these data, providing them freely and openly through an open-source platform to enable

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unprecedented insight into global air quality (https://openaq.org). In this work we use aggregated

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data from OpenAQ and other sources to report and analyze diurnal patterns of PM2.5

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concentration across the world.

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2. Materials and Methods We obtained PM2.5 measurements gathered by OpenAQ for the 2017 calendar year.

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Additional PM2.5 data for 2017 were obtained from websites providing aggregated data from

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China’s national air quality monitoring network (http://data.epmap.org; http://pm25.in). These

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data sets contain PM2.5 measurements collected using a variety of instruments (e.g. BAMs and

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TEOMs) and of measurement protocols; however, no low-cost, mobile, temporary or indoor

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sensors are included. Global hourly precipitation and mixed layer height data at 0.25°x0.31°

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resolution were obtained from the GEOS-FP assimilated meteorological dataset from the NASA

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Global Modeling and Assimilation Office (GMAO). Gridded elevation standard deviation data

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were obtained at half-degree resolution from the International Satellite Land Surface

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Climatology Project initiative II (ISLSCP II) elevation-derived products16. Population density

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data were obtained from the gridded population of the world version 4 (GPW v4) dataset17.

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All PM2.5 measurements were averaged over a period of one hour or less. Hourly data

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points were associated with the local solar time (LST) defined as 𝑇𝐿𝑆𝑇 = 𝑇𝑈𝑇𝐶 +𝐿𝑜𝑛/15

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following Guo et al18. At each site, data were retained only for days with more than 16 out of a

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possible 24 hours. Sites with fewer than 50 days of data were rejected. Sites with mean PM2.5

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concentrations lower than 5 μg/m3 were rejected to reduce measurement error. The resultant data

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set consisted of over 17 million separate hourly measurements from 3110 sites. Precipitation and

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mixed layer height data were sampled at PM2.5 measurement sites for each hour where PM2.5

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measurements were retained, and averaged for each hour across the year.

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Annual site-specific diurnal cycles comprising 24 hourly PM2.5 concentrations were

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calculated as the site mean over the year for each hour of the day, normalized by the site specific

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daily mean PM2.5 value as shown in equation 1. 𝑛𝑑𝑎𝑦𝑠(𝑖)

1 𝑟𝑃𝑀2.5(𝑖,ℎ) = 𝑛𝑑𝑎𝑦𝑠(𝑖)

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∑

𝑑=1

𝑃𝑀2.5(𝑖,ℎ,𝑑) × 𝑛ℎ𝑜𝑢𝑟𝑠(𝑖,𝑑) 𝑛ℎ𝑜𝑢𝑟𝑠(𝑖,𝑑)

∑ℎ = 1

𝑃𝑀2.5(𝑖,ℎ,𝑑)

(1)

rPM2.5(i,h) is the normalized PM2.5 value at site i and hour h, ndays(i) is the number of available days for site i, and nhours(i,d) is the number of available hours for day d at site i. The 3 ACS Paragon Plus Environment


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diurnal variability (DV) was defined as the standard deviation of the normalized diurnal PM2.5

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cycle. Multi-site averages of the diurnal cycle and DV were calculated for interpretation.

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3. Results and Discussion

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Diurnal Variability

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Table 1 summarizes the PM2.5 measurements for each region considered in this study.

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PM2.5 has modest diurnal variation with a global average DV of 13.1%. Regional mean

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concentrations vary by a factor of 6 from North America (8.9 μg/m3 ) to Central Asia (59 μg/m3).

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However, the average diurnal variability exhibits much weaker regional variation ranging from

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12% for East Asia to 19% for Central Asia.

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Figure 1 shows spatial patterns in the annual mean DV and annual mean PM2.5. The

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magnitude of DV is remarkably consistent worldwide, especially considering that the data are

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from multiple instrument types and measurement protocols. The 20th and 80th percentiles of DV

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are 7.6% and 17.8% respectively. The most striking pattern is observed in East Asia, with low

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DV of 5%-10% in southeast China but higher DV of 10%-20% in areas to the more arid North

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and West. DV is fairly consistent within 10%-20% across Europe, however there are clusters of

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high site DV of 20%-30% in southern France and in northern Germany. DV in the western half

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of North America tends to exceed DV in the East. Insufficient data exist to well characterize the

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DV in South America, Africa, Australia, and the Middle East, however the sites that do exist

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exhibit DV of similar magnitude to other regions.

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We find weak global spatial correlation of DV with two static variables, latitude (r =

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0.25) reflecting lower DV in temperate regions, and elevation standard deviation (r = 0.21),

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perhaps reflecting the role of adjacent elevation in inhibiting ventilation of local sources. Sites

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with large DV often occur in mountainous areas, particularly in western North America, the

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Pyranees, and western China (Figure S1). Below we examine the relationships of DV with

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diurnally varying processes.

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Diurnal PM2.5 Cycles

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Figure 2a shows average diurnal PM2.5 cycles as a fraction of the daily mean value. All cycles share a similar pattern, with a morning peak between 07:00-10:00 LST and an early night


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peak between 21:00-23:00 LST. The daily minimum PM2.5 occurs in the afternoon between

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15:00-17:00 LST.

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The consistently shaped PM2.5 diurnal cycle observed across all regions, despite

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significant between-site differences in mean concentration and chemical composition, suggests

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that diurnal PM2.5 cycles are modulated by similar processes worldwide. Similar diurnal patterns

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have been reported for PM10 as well as for primary gas-phase pollutants such as CO19 and

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various aromatic compounds20–22. Figure 2b shows the diurnal cycle of GEOS mixed layer height

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sampled at the PM2.5 measurement sites analogously to Figure 2a. Low afternoon PM2.5 values

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reflect dilution within a deep daytime mixed layer. Conversely, the shallow stable night time

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boundary layer that forms after sun-set allows PM2.5 to accumulate near the surface, contributing

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to higher concentrations at night.

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We also explored potential relationships with precipitation. Figure 2c shows the regional

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diurnal cycles of precipitation sampled at PM2.5 measurement sites analogously to Figure 2a.

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Low afternoon PM2.5 values may also be influenced by wet deposition through increased

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afternoon precipitation. Diurnal precipitation patterns exhibit large regional variation that is

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inconsistent with regional diurnal variation pf PM2.5 (e.g. Europe vs. Other Regions), indicating

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that precipitation is unlikely to be the primary cause of the globally consistent diurnal PM2.5

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cycles in Figure 2a.

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While diurnal PM2.5 cycles generally share similar features, the relative strengths and

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locations of these features differ between regions. The diurnal cycles for North America, Europe,

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and East Asia are comparable in shape and amplitude, but they are temporally shifted relative to

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each other with the morning peak in North America occurring 3 hours earlier than in East Asia.

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Mixed layer height does not explain this regional difference (Figure 2b); regional activity

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patterns may be responsible. The amplitude of the diurnal cycle is largest in Central Asia with

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large morning and night time peaks and a low daytime minimum. The strong diurnal PM2.5 cycle

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in Central Asia is accompanied by a strong diurnal cycle of mixed layer height (Figure 2b). The

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diurnal cycle of mixed layer height is comparable between the remaining regions with the

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smallest amplitude occurring in Europe.

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The increase in DV with increased mixed layer diurnal variation indicates that PM2.5 cycles are influenced by mixed layer height. However, changes in mixed layer height greatly 5 ACS Paragon Plus Environment


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exceed the relatively small overall DV. The mixed layer height more than quadruples from

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sunrise to the afternoon (Figure 2b) whereas the mean DV of PM2.5 is only 13%. This implies

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that other processes compensate for the effects of dilution within the mixed layer on the diurnal

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PM2.5 cycle. One possible source of compensation is interaction with the remnant boundary

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layer, the residual layer containing aerosols previously suspended by vertical mixing but no

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longer contained within the mixed layer. Secondary formation may also dampen the afternoon

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minimum through increased daytime photochemical production23. Other compensation factors

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may include daytime maxima for some sources, such as biogenics24 and emissions25 related to

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human activity patterns.

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Figure S5 shows the normalized and non-normalized diurnal PM2.5 cycles separated by

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season for the sites where data were available for the full year. These sites, mostly in Eastern and

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Central Asia, exhibit large seasonal differences in mean PM2.5 concentration (Figure S5b).

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However, the normalized diurnal PM2.5 cycles remain similar between seasons (Figure S5a) with

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DV ranging from 11.9% in the summer (JJA) to 13.6% in the fall (SON).

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Previous studies have associated the morning and evening peaks of the diurnal PM2.5

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cycle with increased anthropogenic emissions6. Figure S6 shows the diurnal PM2.5 cycles

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separated by site population density, showing that sites with high population density have a

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diurnal PM2.5 cycle with a slightly stronger morning peak. Figure S2 shows that the morning

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peak is attenuated on weekends as compared to weekdays, providing some evidence that human

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activity patterns play a role in enhancing the morning peak. However this effect is small, and the

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effects of anthropogenic emissions on the evening peak are harder to discern due to the

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coincidence of the evening rush hour with the collapse of the daytime mixed layer.

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While DV is consistent between sites across a wide range of mean PM2.5 concentrations,

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sites with mean PM2.5 concentrations exceeding 80 μg/m3 exhibit diurnal PM2.5 cycles with

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increased amplitude (Figure S3a). These sites are associated with a stronger cycle of mixed layer

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height (Figure S3b). Some sites also show diurnal PM2.5 cycles that are very different from the

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global average. For example, in Mexico City PM2.5 levels are highest at midday despite a very

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strong diurnal cycle of mixing layer height (Figure S4) reflecting unique site photochemical

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production and/or emission patterns. However, such anomalous sites are sufficiently rare that

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diurnal PM2.5 cycles remain highly consistent across regions (Figure 2a). 6 ACS Paragon Plus Environment

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Outlook

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Analysis of over 17 million hourly measurements from 3110 sites revealed that regional

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diurnal variability ranged from 12% in East Asia to 19% in Central Asia, despite large regional

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differences in regional mean PM2.5 concentration of 9 μg/m3 in North America to 59 μg/m3 in

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Central Asia. This remarkable global homogeneity in diurnal PM2.5 cycles suggests the influence

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of common factors including the diurnal cycle of mixed layer depth modulated by other

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processes such as diurnally varying emission patterns. Subtle regional differences offer insight

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into regional processes. Further work is needed to elucidate the effects of wet deposition and

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secondary formation on the diurnal PM2.5 cycle. This work also demonstrates the utility of aggregated datasets from organizations such as

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OpenAQ for providing novel insight into the factors affecting air quality on hourly timescales

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across the world. These datasets should be useful for a number of further applications including

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evaluating the ability of chemical transport models to represent the observed diurnal variation of

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PM2.5.

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Acknowledgements

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The authors are grateful to the groups measuring and sharing local PM2.5 concentrations

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and the volunteer community members of OpenAQ, who help build the open data platform by

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their open-source contributions. This work was supported by the Natural Sciences and

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Engineering Research Council of Canada (NSERC).

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The authors declare no competing financial interests.

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Supporting Information Available: additional figures as described in the text. This material is

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available free of charge via the Internet at http://pubs.acs.org.

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Table 1. Summary of PM2.5 data from OpenAQ and the Chinese air quality monitoring network. regiona

number of sites

number of hourly data points (millions)

average of annual site means ± standard deviation (μg/m3 )

average diurnal variability (% )

North America

782

3.0

9.1 ± 3.6

13.3

Europe

625

1.7

11.6 ± 4.3

14.8

East Asia

1530

12.0

44.8 ± 15.4

11.7

Central Asia

101

0.67

58.7 ± 34.4

18.6

Other

72

0.21

15.5 ± 11.5

16.9

Global

3110

17.6

28.9 ± 21.9

13.1

aRegions

defined in Figure 1

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Figure 1. (a) Annual mean diurnal variability of PM2.5. (b) Annual mean PM2.5 concentration (log scale). Boxes define the regions in this study. All other sites are included as ‘other regions.’ Note that some points are obscured due to overlap between neighbouring sites. 8 ACS Paragon Plus Environment

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Figure 2. (a) Regional average diurnal PM2.5 cycles expressed as a ratio of the daily mean and

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separated by region. (b) Regional average diurnal cycles of mixed layer height (GEOS). (c)

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Regional average diurnal cycles of precipitation (GEOS). 9 ACS Paragon Plus Environment


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Diurnal Patterns in Global Fine Particulate Matter Concentration

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