Light pollution is considered to be artificial or anthropogenic introduction of light in what would normally be a night setting. Light pollution has grown significantly throughout the twentieth century as cheap, constant light became more possible in urban areas. However, light pollution has the potential to negatively affect our health, ecology, and enjoyment of our environment. Light pollution that is particularly intense can cause the most damage to our health and environment. This has led to a number of remedies, ranging from shielding of light to preventing upward light from being emitted.
Monitoring Light Pollution with GIS and Remote Sensing
Similar to noise pollution, light pollution has a strong correlation to growing urbanism and, because of rapidly growing populations in parts of the world and increasing access to a variety of technologies and electricity, light pollution has in recent times grown almost exponentially. Light pollution is also used as a proxy for measuring urban pollution or reduction in quality of life. Spatial technologies, including remote sensing and GIS, have been used to monitor light pollution and its associated degradation effects. Sensors such as the Operational Linescan System (OLS) allow light to be compounded in measurements, allowing different spatial scale measurements to be possible in urban to regional settings. Visibility analysis of light pollution provides spatial techniques that look at the effect of direct and indirect light, where direct light includes optical contact with artificial light and indirect light pollution includes optical contact with sky glow, or reflected light pollution. This type of measurement allows one to see how changes of light pollution can occur over year-to-year scales or longer, such as decadal or more timescales.
In Europe over the last decade, increasing awareness of the negative effects of light pollution have had noticeable, spatial results in measuring light pollution. Northwestern Europe, such as in Belgium and Sweden, indicate some clear trends of diminishing their light pollution. This is likely from government efforts to dim lights at night, such as in street lighting. Other areas, such as in part of the former Soviet Union, light pollution may have declined due to economic and population decline in places.
Using Light Pollution to Study Electricity Consumption
While light pollution could affect human health and the ecology, it is also a useful measure to calculate electricity consumption across a country. Overglow is something that can obscure the measurement of energy consumption, as reflected light might not represent light that is actually emitted. Using measures for intensity of light and its source, an Overglow Removal Model (ORM) allows indirect light to be removed and, therefore, electric consumption to be more accurately estimated. The results could be used for electric or light reducing measures or even for where investment for increased economic activity might be more desirable.
How Light Pollution Affects the Environment
Recent studies have also looked at the effects of artificial lights on the environment. For instance, in marine protected areas, encroaching artificial light can disturb a variety of animals, including nesting sea turtles and other creatures. Spatial relationships showing economic development in places, particularly around marinas, demonstrate encroachment into marine protected regions around the world. This has led to proposals to apply counter methods including dimming light, using light shades, and even applying light at different spectra to limit the most damaging spectra from being applied in sensitive regions.
Overall, we now have a better understanding of where light pollution is likely to occur. Spatial relationships between economic activity, including GDP for countries, and population have had the closest relationship to why there is increased light pollution in places. However, as the study in Europe has suggested, once some countries become aware of the problem and take measures, then we can begin to see a noticeable decline or reversal in light pollution in relatively affluent and economically growing places. In effect, we now have better measures to predict where light pollution is likely to occur and, therefore, can take measures to limit its growth.
 For more on types of light pollution and remedies, see: Mizon, B. (2002) Light Pollution: Responses and Remedies. [Online]. London, Springer London.
 For more on using this type of sensor to monitoring light pollution in China, see: Han, P., Huang, J., Li, R., Wang, L., et al. (2014) Monitoring Trends in Light Pollution in China Based on Nighttime Satellite Imagery. Remote Sensing. [Online] 6 (6), 5541–5558.
 For a common method for measuring light pollution using spatial approaches, see: Chalkias, C., Petrakis, M., Psiloglou, B. & Lianou, M. (2006) Modelling of light pollution in suburban areas using remotely sensed imagery and GIS. Journal of Environmental Management. [Online] 79 (1), 57–63.
 For more on measured spatial patterns of light pollution, see: Bennie, J., Davies, T.W., Duffy, J.P., Inger, R., et al. (2015) Contrasting trends in light pollution across Europe based on satellite observed night time lights. Scientific Reports. [Online] 4 (1).
 For more on the Overglow Removal Model, see: Townsend, A.C. & Bruce, D.A. (2010) The use of night-time lights satellite imagery as a measure of Australia’s regional electricity consumption and population distribution. International Journal of Remote Sensing. [Online] 31 (16), 4459–4480.
 For more on the study looking at the effects of light pollution on marine protected areas, see: Davies, T.W., Duffy, J.P., Bennie, J. & Gaston, K.J. (2016) Stemming the Tide of Light Pollution Encroaching into Marine Protected Areas: Light pollution in marine protected areas. Conservation Letters. [Online] 9 (3), 164–171. Available from: doi:10.1111/conl.12191.
 For more on variable relationships with light pollution, see: Gallaway, T., Olsen, R.N. & Mitchell, D.M. (2010) The economics of global light pollution. Ecological Economics. [Online] 69 (3), 658–665.
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