References
Chapman S (1930) A theory of upper-atmospheric ozone. Edward Stanford, London
Cooper OR, Langford AO, Parrish DD et al (2015) Atmosphere. Challenges of a lowered
U.S. ozone standard. Science 348:1096–1097
Duncan BN, Lamsal LN, Thompson AM et al (2016) A space-based, high-resolution view of
notable changes in urban NO x pollution around the world (2005–2014). J Geophys Res
121:976–996
Fang JY, Guo ZD, Hu HF et al (2014) Forest biomass carbon sinks in East Asia, with special
reference to the relative contributions of forest expansion and forest growth. Glob Chang Biol
20:2019–2030
Feng ZZ, Kobayashi K (2009) Assessing the impacts of current and future concentrations of surface
ozone on crop yield with meta-analysis. Atmos Environ 43:1510–1519
Feng ZZ, Wang SG, Szantoi Z et al (2010) Protection of plants from ambient ozone by applications
of ethylenediurea (EDU): a meta-analytic review. Environ Pollut 158:3236–3242
Feng ZZ, Pan J, Kobayashi et al (2011) Differential responses in two varieties of winter wheat to
elevated ozone concentration under fully open-air field conditions. Glob Chang Biol
17:580–591
Feng ZZ, Sun JS, Wan WX et al (2014) Evidence of widespread ozone-induced visible injury on
plants in Beijing, China. Environ Pollut 193:296–301
Feng ZZ, Hu EZ, Wang XK et al (2015a) Ground-level O 3 pollution and its impacts on food crops
in China: a review. Environ Pollut 199:42–48
Feng ZZ, Liu XJ, Zhang FS (2015b) Air pollution affects food security in China: taking ozone as an
example. Front Agr Sci Eng 2:152–158
Feng ZZ, Büker P, Pleijel H et al (2018) A unifying explanation for variation in ozone sensitivity
among woody plants. Glob Chang Biol 24:78–84
Frei M, Tanaka JP, Chen CP et al (2010) Mechanisms of ozone tolerance in rice: characterization of
two QTLs affecting leaf bronzing by gene expression profiling and biochemical analyses. J Exp
Bot 61:1405–1417
Fig. 7.12 Meta-analysis of the effect of non-filtered air (NF) vs. charcoal-filtered air (CF) for
agronomically important wheat variables. Values in brackets indicate number of NF-CF comparisons for each variable. Error bars are 95% confidence intervals. (This figure was adapted from
Pleijel et al. (2018) with permission by Elsevier)
152
Z. Feng et al.
Chapman S (1930) A theory of upper-atmospheric ozone. Edward Stanford, London
Cooper OR, Langford AO, Parrish DD et al (2015) Atmosphere. Challenges of a lowered
U.S. ozone standard. Science 348:1096–1097
Duncan BN, Lamsal LN, Thompson AM et al (2016) A space-based, high-resolution view of
notable changes in urban NO x pollution around the world (2005–2014). J Geophys Res
121:976–996
Fang JY, Guo ZD, Hu HF et al (2014) Forest biomass carbon sinks in East Asia, with special
reference to the relative contributions of forest expansion and forest growth. Glob Chang Biol
20:2019–2030
Feng ZZ, Kobayashi K (2009) Assessing the impacts of current and future concentrations of surface
ozone on crop yield with meta-analysis. Atmos Environ 43:1510–1519
Feng ZZ, Wang SG, Szantoi Z et al (2010) Protection of plants from ambient ozone by applications
of ethylenediurea (EDU): a meta-analytic review. Environ Pollut 158:3236–3242
Feng ZZ, Pan J, Kobayashi et al (2011) Differential responses in two varieties of winter wheat to
elevated ozone concentration under fully open-air field conditions. Glob Chang Biol
17:580–591
Feng ZZ, Sun JS, Wan WX et al (2014) Evidence of widespread ozone-induced visible injury on
plants in Beijing, China. Environ Pollut 193:296–301
Feng ZZ, Hu EZ, Wang XK et al (2015a) Ground-level O 3 pollution and its impacts on food crops
in China: a review. Environ Pollut 199:42–48
Feng ZZ, Liu XJ, Zhang FS (2015b) Air pollution affects food security in China: taking ozone as an
example. Front Agr Sci Eng 2:152–158
Feng ZZ, Büker P, Pleijel H et al (2018) A unifying explanation for variation in ozone sensitivity
among woody plants. Glob Chang Biol 24:78–84
Frei M, Tanaka JP, Chen CP et al (2010) Mechanisms of ozone tolerance in rice: characterization of
two QTLs affecting leaf bronzing by gene expression profiling and biochemical analyses. J Exp
Bot 61:1405–1417
Fig. 7.12 Meta-analysis of the effect of non-filtered air (NF) vs. charcoal-filtered air (CF) for
agronomically important wheat variables. Values in brackets indicate number of NF-CF comparisons for each variable. Error bars are 95% confidence intervals. (This figure was adapted from
Pleijel et al. (2018) with permission by Elsevier)
152
Z. Feng et al.