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Overview of air pollutants in Shanghai during 2015–2018
The average mass concentrations of the target pollutants during 2015–2018 were analyzed. We used the cumulative distribution of daily average values of PM2.5, PM10, NO2, SO2, CO, and O3_8h to determine the number of days during which Shanghai municipality was exposed to air pollution (Fig. 1)24. For at least some half-days in 2015 (2016, 2017, 2018), Shanghai municipality was exposed to average values higher than 59 (50, 45, 40) μg m−3 for PM2.5, 52 (48, 47, 40) μg m−3 for PM10, 45 (43, 47, 44) μg m−3 for O3_8h, 48 (45, 47, 44) μg m−3 for NO2, 13 (12, 9, 8) μg m−3 for SO2, and 18 (18, 18, 15) mg m−3 for CO. This indicates a decrease in the number of days per year in which Shanghai residents were exposed to high concentrations of PM2.5, PM10, NO2, SO2, and CO.
Figure 1

(a–f) Cumulative distribution of daily average mean concentrations of air pollutants in Shanghai municipality.

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Temporal variations in air pollutants
Following implementation of the six-round, 3-year environmental protection action plan, ambient air quality in Shanghai municipality has improved slightly. In 2018, the average annual concentration of SO2 and PM10 in Shanghai municipality was 10 μg m−3 and 51 μg m−3 respectively, the 90th percentile of O3_8h concentration was 160 μg m−3, and daily CO concentration was within the range 0.4–2.0 mg m−3. All these concentrations met the national Level I or Level II for annual mean ambient air quality. However, the average annual concentration of NO2 and PM2.5 in the city in 2018 was 42 μg m−3 and 36 μg m−3, respectively, which did not meet the Level II annual mean level air quality standard. Moreover, monitoring data for the past 4 years show that the annual mean concentrations of NO2 and PM2.5 in Shanghai are generally declining, but they still exceed the national Level II air quality standards. The daily maximum 8-h average, 24-h average, and annual mean concentrations of six air pollutants in Shanghai municipality during 2015–2018 are summarized in Fig. 2. Compared with 2015, the average concentration in 2018 decreased by 32.08%, 26.09%, 0.62%, 41.18%, 8.70%, and 22.09% for PM2.5, PM10, O3_8h, SO2, NO2, and CO, respectively. The large decrease in SO2 in the air Shanghai municipality was consistent with the overall trend in annual mean concentration of SO2 in China8. This indicates effective control of combustion emissions and implementation of desulfurization systems8,31. Our results also indicated that more than 70% of the total mass of PM10 was composed of PM2.5, which is close to the ratio reported in previous studies8,24. The decreases in CO and NO2 concentrations were mainly attributable to effective regulation of coal combustion emissions and traffic-related emissions8,31,32,33. The reductions amplitudes were lower for CO and NO2 compared with PM2.5, PM10, and SO2, which may be related to the rapid increase in vehicles in Chinese cities8. No clear decrease was observed for the 90th percentile of O3_8h concentration in this study. Air pollution has gradually changed from the conventional coal combustion type to mixed coal combustion/motor vehicle emission type3, reflecting the rapid increase in the number of motor vehicles in Shanghai municipality34. This poses enormous challenges for air pollution control and environmental management.
Figure 2

Temporal variations in 24-h average concentrations and annual mean concentrations of air pollutants in Shanghai municipality, 2015–2018.

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Major pollutants and non-attainment days
The number of days meeting the mean concentration limits of ‘Chinese ambient air quality standards’ (CAAQS) in Shanghai municipality during 2015–2018 was examined (Fig. 3). In 2015 (2016, 2017, 2018), 18.6% (27.5%, 33.6%, 41.5%), 77.9% (85.3%, 92.6%, 91.5%), 35.8% (40.1%, 35.2%, 41.0%), 99.5% (100%, 100%,100%), 99.7 (100%, 100%, 100%), and 58.4% (67.2%, 57.0%, 60.8%) of days met the concentration limit in CAAQS Grade II for 24-h average PM2.5, PM10, NO2, SO2, CO, and maximum 8-h average O3. Compared with 2015, the number of days in 2018 that met the level in CAAQS Grade II increased by 124.3%, 17.5%, 4.1%, 14.5%, 0.5%, and 0.3% for PM2.5, PM10, O3_8h, SO2, NO2, and CO, respectively. The number of days with excellent air quality increased from 55 in 2015 to 93 in 2018, while the number of days with ‘good’ air quality remained consistent at 203 days between 2015 and 2018.
Figure 3

Number of days per year on which each pollutant was designated a “major pollutant” (different shapes) and air quality level (different colors) in Shanghai municipality.

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The most frequent “major pollutant” in Shanghai municipality was O3, followed by PM2.5 and then NO2 and PM10. In comparison, SO2 and CO were the “major pollutant” considerably less frequently. The number of days on which PM2.5, O3, NO2, and PM10 was designated the “major pollutant” was 120 (104, 67, 61), 110 (84, 126, 113), 50 (67, 79, 63) and 16 (13, 13, 14) in 2015 (2016, 2017, 2018), respectively. The low incidence of SO2 as a “major pollutant” again indicated effective control of coal combustion and implementation of desulphurization systems8,31. Compared with 2015, the incidence of O3 as a major pollutant in Shanghai increased to reach its highest value in 2017. This is consistent with the 90th percentile of O3_8h concentration, which also peaked in 2017. Previous studies have suggested that O3 is a complex secondary pollutant related to solar radiation, NOx, volatile organic compounds (VOC), and vertical transport in the boundary layer8, factors that are difficult to control effectively35,36. While the number of polluted days with PM2.5 concentrations over 75 μg m−3 decreased from 2015 to 2018, the complex mixture of PM2.5 and O3 in the air is still a challenge to continuous improvement of air quality in Shanghai municipality8,24.
There were seasonal variations in the concentrations of each pollutant (Fig. 4a), and thus the days on which the air quality standard was exceeded (non-attainment days) were not equally distributed throughout the year (Fig. 4b), which is consistent with findings in previous studies24,37. November, December, January, February, and March were the dominant months with non-attainment days for PM2.5 in Shanghai municipality, while April, May, June, July, August, and September were the dominant months with non-attainment days for O3_8h. Overall, winter months had the largest number of polluted days and highest mean concentration of PM2.5, followed by spring, autumn, and summer, which is consistent with previous findings16. This trend has been mainly attributed to coal-fired heating of buildings16,38,39,40. Summertime O3 pollution in Shanghai was much more severe than in the other seasons (Fig. 4b), and the probability of O3_8h exceeding the CAAQS Grade II value was highest in July (11.25 ± 5.85 day), followed by August (6.25 ± 4.65 day), May (5.75 ± 3.2 day), and June (5.5 ± 1.29 day). This is consistent with findings in previous studies that summer is the O3 episode season in Chinese megacity clusters41,42. Polluted days with NO2  > 80 μg m−3 were mainly observed during winter and spring. The low probability of SO2 exceeding the CAAQS Grade II value reflected the stringent SO2 emission regulations in Shanghai municipality31.
Figure 4

(a) Average concentration of the pollutants PM2.5, PM10, SO2, and NO2 and (b) percentage of non-attainment days and major pollutant on polluted days in each month during 2015–2018.

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Correlations between air pollutants
Different air pollutants were significantly correlated (p  More

Assessment of data quality
National Allergy Bureau pollen concentration data quality
To assess the quality of NAB data overall, we analyzed gaps in data recording and percentages of missing data in daily NAB measurements from each station from January to December of each year. Availability of pollen concentration data varied widely by station, with percent of days per year missing pollen data ranging from 0% (e.g. San Antonio, TX; 2012) up to 100% (e.g. Oklahoma City, OK; 2014) (Supplementary Fig. 5A). Common days missing data were at the beginning of the year, the end of the year, and on weekends (data not shown). Although NAB directs its certified pollen counting stations to collect data for a minimum of 3 days per week, gaps in pollen collection within 10 days before and after the first recorded high pollen concentration (200 grains/m3) spanned up to 10 consecutive days (Supplementary Fig. 5B). Over the span of the year, the median gap between measurements across station-years was 5 days (IQR = 3.12). The date of first available pollen concentration data ranged from day 1 of the year to day 96 with a median day of 3 (IQR = 1.27) (Supplementary Fig. 5C). For the majority of station-years (64.5%), the first day of the first recorded data for the year was the same as the first day with a non-zero pollen count.
Google trends search data quality
We analyzed GT daily data quality per DMA region during the early pollen season, from January to June of each year. The percent of missing days of GT data ranged from 0–93% (lowest missing from San Jose CA 2013 and highest missing from Midland TX 2012, respectively) with median and IQR = 33% (8–51%) (Supplementary Fig. 6A). Earlier years of GT data had more daily search volumes not quantified (referred to here as “missing”) due to lower search volumes and not meeting Google’s threshold for inclusion (Supplementary Fig. 6B). Variation was observed between GT download iterations, as GT provides a random sample of its data for each download (Supplementary Fig. 2A,B).
Factors associated with data quality
Biogeography and population characteristics were assessed for their impact on data quality, specifically overall ecoregion classification, total annual precipitation and mean spring temperature (chosen for their likely impact pollen production and seasonality34), as well as TV-homes, a combinatorial metric for population size and media use.
With respect to ecoregion, the majority of NAB stations were classified as Eastern Temperature Forests (67.6%) or Great Plains (21.6%). Other ecoregions each represented 5% or less of NAB stations: Marine West Coast Forest, Mediterranean California, and Northwestern Forested Mountains. U.S. ecoregions not represented by NAB stations included: Northern Forests (as in Vermont), Tropical Wet Forests (as in southern Florida), North American Deserts (as in Nevada), Southern Semi-Arid Highlands (as in southeastern Arizona), and Temperate Sierras (as in southwestern New Mexico). As a whole, NAB stations in Great Plains ecoregions had slightly higher data quality (p  More

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