在北京上一次遭遇史上最嚴重空氣污染之后沒幾天，IBM就開始著手為城市打造更加強大的污染監測和預報體系，此舉引發了廣泛關注。藍色巨人的中國研究實驗室正聯手北京環境保護局合作研發一款新系統。該公司還將與同樣受到霧霾困擾的約翰內斯堡和新德里進行合作。

據IBM綠色地平線（Green Horizons）污染與可再生能源預報項目首席研究員亨德里克?哈曼介紹，北京現有的污染監測手段成本太高，精確度也不足。偌大的城市只有幾十個監測站，盡管它們的測量十分準確，想要得出北京污染的詳細情況卻還不夠。

作為這些監測站的補充，IBM和北京環境保護局打算建立由幾百甚至幾千個廉價、聯網、易于維護、鞋盒大小的感應器組成的監測網絡。

感應器獲得的數據將通過整合天氣模式，甚至空氣化學反應的計算機模型進行分析，最終生成精確到1平方千米的72小時污染預報。

物聯網和大數據的這種結合，正是城市智能化的縮影。

哈曼表示：“顯而易見的是，如果不能測量，就無法治理。”

在交通堵塞和天氣的問題上，這顯然是真理，不過這里還有個疑問——城市“治理”污染究竟是什么意思？相較于擺脫污染，測量污染究竟有多重要？

表面上看，空氣質量監測似乎只是一種反應式措施。當地的粒狀物污染因為天氣原因，每天都有所不同，哈曼甚至將嚴重的污染比作臺風：“我面對臺風無能為力，但我可以保護市民的安全。”在北京，保護市民免受污染就意味著學校停課放假，大型建筑調整進氣口，政府發布警告建議人們減少室外活動。

當然，污染與臺風不完全是一回事——首先，污染大部分是由人類造成的，人類也有一定的能力控制它，甚至每天都能控制。目前，北京的空氣預警系統給予市政府很大的權力，比如停止建設，減少工廠作業（包括污染嚴重的火力發電站），限制車輛出行、爆竹燃放和燒烤活動。

這些限制措施會帶來巨大的經濟損失，哈曼博士表示，IBM研發的顆粒物污染預測會讓政府的措施更具針對性——例如，如果關閉城市里某些區域的火力發電站對于減輕霧霾具有最大成效，那么就只需關閉這些發電站。

即便是短期關閉火電站，聽起來也像是渡過霧霾高峰期的臨時緩解措施，沒有改變根本問題。不過，哈曼博士相信，通過基于情景的預報，以及公眾意識的提高，更好的數據從長期上會改善污染狀況。

該系統強大的建模能力，可以就某些項目提供更為詳盡的成本和收益分析，例如升級公共交通工具的替代能源，加裝煙囪過濾器，提高能源效率等。哈曼表示：“看看洛杉磯等城市（這些城市抗擊霧霾問題已有數十年），隨著對污染問題的測量和了解，他們逐步推行各種不同的措施。”

哈曼認為，更好的測量手段通過提供市民可以關注的污染基準，還能起到自下而上的監督作用。這能讓城市居民個人的咳嗽和喘氣變成一個群體關心的問題，換句話說，這些信息有助于將污染問題放在政治層面上解決。

不過在北京，官員還沒有接受這個理念，公民時常難以獲取污染數據信息。盡管中國已經有了采用其他能源的傾向，但依舊在很大程度上依賴火力發電。（財富中文網）

譯者：嚴匡正

審校：任文科

Just days after Beijing’s worst day of air pollution in recent history, IBM is highlighting its efforts to build stronger pollution monitoring and forecasting systems for cities. Big Blue’s China Research Lab has been developing a new system with Beijing’s Environmental Protection Bureau, and on Wednesday announced partnerships with similarly smoggy cities including Johannesburg and Delhi.

According to Dr. Hendrik Hamann, a lead researcher with IBM’s Green Horizons pollution and renewable energy forecasting program, Beijing’s current pollution monitoring methods are both too costly and too imprecise. Only a few dozen monitoring stations are spread out over the huge city, and though they’re highly accurate, there aren’t enough of them to get a detailed picture. IBM IBM 5.04% and BEPB want to supplement those with a network of hundreds or even thousands of inexpensive, networked, low-maintenance sensors about the size of a shoebox.

Data from the sensors would be analyzed, using computer models incorporating weather patterns and even airborne chemical reactions, to produce 72-hour pollution forecasts detailed down to 1km-square areas.

It’s a melding of the Internet of things and big data that epitomizes the movement to smarter cities.

“Stating the obvious,” says Hamann, “What you can’t measure, you can’t manage.”

That’s certainly true of things like traffic congestion and local weather, but it demands the question—what exactly does it mean for a city to ‘manage’ pollution? And how important is it to measure it, compared to just getting rid of it?

On the surface, air quality monitoring can seem merely reactive. Local particulate pollution can spike from one day to the next due to weather patterns, and Hamann even compares a bad pollution day to a hurricane: “I can’t do anything about the hurricane, but I can protect the well-being of citizens.” In Beijing, protecting citizens against pollution means cancelling school for young kids, regulating air intake in big buildings, and government warnings against outdoor activities.

But of course, pollution isn’t quite like a hurricane—humans cause most of it in the first place, and they have some ability to control it, even day to day. Currently, Beijing’s air alert system gives the city broad powers to shut down construction and curtail industrial operations (including its nasty coal-fired power grid), and restrict driving, fireworks, and barbecues.

Those controls have substantial economic costs, and Dr. Hamann says the more granular forecasting IBM has developed would allow them to be more targeted—say, shutting down coal plants in only the parts of a city where it would do the most good.

Even temporarily shutting down coal plants, though, might sound like more short-term mitigation—a way to smooth out the smoggy peaks without changing the underlying problem. But Dr. Hamann believes better data will lead to longer-term improvements through a combination of scenario-based forecasting and public awareness.

Stronger modelling allows more detailed analysis of the costs and benefits of, say, upgrading public transportation to alternative fuels, adding smokestack filters, and increasing energy efficiency. “Look at places like . . . Los Angeles,” Hamann says, which battled its own smog problem for decades. “Through measurement, understanding of the problem, step by step, different actions were taken.”

Hamann thinks better measurement also works from the bottom up, by providing benchmarks that citizens can actually keep track of. That helps synthesize city dwellers’ individual coughs and wheezes into a collective project—in other words, information helps politicize pollution.

But in Beijing, officials haven’t embraced that idea, restricting access to information including the U.S. Embassy’s pollution readings. Though that mindset is improving, China is extremely dependent on coal for power. IBM’s super-accurate data won’t be able to fulfill its promise if those in charge prefer to keep things cloudy.